In order to effectively restore color noisy images with the mixture of Gaussian noise and impulse noise,a new algorithm is proposed using the quaternion-based holistic processing idea for color images.First,a color im...In order to effectively restore color noisy images with the mixture of Gaussian noise and impulse noise,a new algorithm is proposed using the quaternion-based holistic processing idea for color images.First,a color image is represented by a pure quaternion matrix.Secondly,according to the different characteristics of the Gaussian noise and the impulse noise,an algorithm based on quaternion directional vector order statistics is used to detect the impulse noise. Finally,the quaternion optimal weights non-local means filter （QOWNLMF）for Gaussian noise removal is improved for the mixed noise removal.The detected impulse noise pixels are not considered in the calculation of weights.Experimental results on five standard images demonstrate that the proposed algorithm performs better than the commonly used robust outlyingness ratio-nonlocal means （ROR-NLM）algorithm and the optimal weights mixed filter （OWMF）.展开更多
A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected...A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected as the features. The four features are combined together as a parameter to detect the edges of color images. Experimental results show that the method can inhibit noisy edges and facilitate the detection for weak edges. It has a better performance than conventional methods in noisy environments.展开更多
In the paper,a convolutional neural network based on quaternion transformation is proposed to detect median filtering for color images.Compared with conventional convolutional neural network,color images can be proces...In the paper,a convolutional neural network based on quaternion transformation is proposed to detect median filtering for color images.Compared with conventional convolutional neural network,color images can be processed in a holistic manner in the proposed scheme,which makes full use of the correlation between RGB channels.And due to the use of convolutional neural network,it can effectively avoid the one-sidedness of artificial features.Experimental results have shown the scheme’s improvement over the state-of-the-art scheme on the accuracy of color image median filtering detection.展开更多
Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompress...Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.展开更多
In modern society,information is becoming increasingly interconnected through networks,and the rapid development of information technology has caused people to pay more attention to the encryption and the protection o...In modern society,information is becoming increasingly interconnected through networks,and the rapid development of information technology has caused people to pay more attention to the encryption and the protection of information.Image encryption technology is a key technology for ensuring the security performance of images.We extracted single channel RGB component images from a color image using MATLAB programs,encrypted and decrypted the color images by randomly disrupting rows,columns and regions of the image.Combined with histograms and the visual judgments of encryption images,it is shown that the information of the original image cannot be obtained from the encryption image easily.The results show that the color-image encryptions with the algorithm we used have good effect and fast operation speed.Thus this algorithm has certain practical value.展开更多
In complex media scattering,multiple scattering severely degrades the optical wavefront and results in blurred images,while the spectral distortion caused by the scattering effect leads to severe color distortion.Achi...In complex media scattering,multiple scattering severely degrades the optical wavefront and results in blurred images,while the spectral distortion caused by the scattering effect leads to severe color distortion.Achieving color high-resolution imaging through scattering media remains a significant challenge.Here,we propose a broadband,polarization-based method for color high-resolution imaging through scattering media.This approach enables high-resolution reconstruction by effectively separating the speckle illumination pattern from the mixed-scattering field information,leveraging polarization common-mode characteristics.Concurrently,it incorporates chromatic balance compensation to correct spectral aliasing in the scattered light field,enabling color high-resolution imaging through complex scattering media.To further optimize color distortion caused by scattering,a compensation strategy combining color constancy and white balance theory is adopted.Experimental results demonstrate that the proposed method significantly enhances both spatial resolution and color fidelity across various scattering conditions and target materials,showcasing strong adaptability and robustness.This approach provides an effective solution for achieving high-resolution color optical imaging in complex scattering environments.展开更多
Abstract:Stephen Crane was an outstanding American novelist,poet,and journalist.He achieved great success in his literary works during his brief career.Crane’s most well-known work,The Red Badge of Courage,is commonl...Abstract:Stephen Crane was an outstanding American novelist,poet,and journalist.He achieved great success in his literary works during his brief career.Crane’s most well-known work,The Red Badge of Courage,is commonly believed to be the first great novel of the American Civil War,largely because of its vivid and detailed description of the experience of warfare.This paper analyzes the images of color,animal and machine,which convey Crane’s thoughts of war:war is full of chaos,brutality,and confusion,without any romantic elements or heroism.展开更多
Methods for pressure sore monitoring remain both a clinical and research challenge.Improved methodologies could assist physicians in developing prompt and effective pressure sore interventions.In this paper a techniqu...Methods for pressure sore monitoring remain both a clinical and research challenge.Improved methodologies could assist physicians in developing prompt and effective pressure sore interventions.In this paper a technique is introduced for the assessment of pressure sores in guinea pigs,using captured color images.Sores were artificially induced,utilizing a system particularly developed for this purpose.Digital images were obtained from the suspicious region in days 3 and 7 post-pressure sore generation.Different segments of the color images were divided and labeled into three classes,based on their severity status.For quantitative analysis,a color based texture model,which is invariant against monotonic changes in illumination,is proposed.The texture model has been developed based on the local binary pattern operator.Tissue segments were classified,using the texture model and its features as inputs to a combination of neural networks.Our method is capable of discriminating tissue segments in different stages of pressure sore generation,and therefore can be a feasible tool for the early assessment of pressure sores.展开更多
In this article, a new way to evaluate the quality of color images is proposed, in which the properties of human vision psychology, objective fidelity, edge information, and color distortion will be combined through u...In this article, a new way to evaluate the quality of color images is proposed, in which the properties of human vision psychology, objective fidelity, edge information, and color distortion will be combined through utilizing 3-D matrix transform. There exists color redundancy and structural similarity between three different frames of a color image, the definition of vision properties will be measured by 3-D submatrix integration transform （SIT）, in which three color components are integrated into one model and color redundancy can be exploited fully. The simulation results show that the measure index is very effective and objective in accord with vision properties.展开更多
Based on the idea of second generation image coding, a novel scheme for coding still images is pre- sented.At first, an image was partitioned with a pulse-coupled neural network; and then an improved chain code and th...Based on the idea of second generation image coding, a novel scheme for coding still images is pre- sented.At first, an image was partitioned with a pulse-coupled neural network; and then an improved chain code and the 2D discrete cosine transform was adopted to encode the shape and the color of its edges respectively.To code its smooth and texture regions, an improved zero-trees strategy based on the 2nd generation wavelet was chosen.After that, the zero-tree chart was selected to rearrange quantified coefficients.And finally some regulations were given according to psychology of various users.Experiments under noiseless channels demonstrate that the proposed method performs better than those of the current one, such as JPEG, CMP, EZW and JPEG2000.展开更多
A method for shadow detection and compensation for color aerial images is presented.It is considered that the intensity value of each image pixel is the product of illumination function and ground object reflection,an...A method for shadow detection and compensation for color aerial images is presented.It is considered that the intensity value of each image pixel is the product of illumination function and ground object reflection,and the shadowed regions on the image are mainly caused by the short of illumination,so the information compensation for the shadowed regions should concentrate on the illumination adjustment of concerned area on the basis of the analysis of whole image.The shadow detection and compensation procedure proposed by this paper consists of four steps.展开更多
The classical vector median filter (VMF) has been widely used to remove impulse noise from color images. However, since the VMF cannot identify thin lines during the denoising process, many thin lines may be removed o...The classical vector median filter (VMF) has been widely used to remove impulse noise from color images. However, since the VMF cannot identify thin lines during the denoising process, many thin lines may be removed out as noise. This serious problem can be solved by a newly proposed filter that uses a noise detector to find these thin lines and then keep them unchanged. In this new approach, the noise detection scheme applied on a current processed pixel is realized through counting the close pixels in its eight neighbor positions and the expanded window to see whether the current pixel is corrupted by impulse noise. Based on the previous outputs, our algorithm can increase the performance in detecting and canceling the impulse noise. Extensive ex- periments indicate that this approach can be used to remove the impulse noise from a color image without distorting the useful information.展开更多
Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a rand...Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a random color filter array(RCFA)to modulate Fourier patterns.A three-step phase-shifting technique reconstructs the Fourier spectrum,followed by an RCFA-based demosaicing algorithm to recover color images.Compared to traditional color FSI based on Bayer color filter array schemes(FSI-BCFA),our approach achieves superior separation between chrominance and luminance components in the frequency domain.Simulation results demonstrate that the FSI-RCFA method achieves a lower mean squared error(MSE),a higher peak signal-to-noise ratio(PSNR),and superior noise resistance compared to FSI-BCFA,while enabling direct single-channel pixel measurements for targeted applications such as agricultural defect detection.展开更多
Image coloring is an inherently uncertain and multimodal problem.By inputting a grayscale image into a coloring network,visually plausible colored photos can be generated.Conventional methods primarily rely on semanti...Image coloring is an inherently uncertain and multimodal problem.By inputting a grayscale image into a coloring network,visually plausible colored photos can be generated.Conventional methods primarily rely on semantic information for image colorization.These methods still suffer from color contamination and semantic confusion.This is largely due to the limited capacity of convolutional neural networks to learn deep semantic information inherent in images effectively.In this paper,we propose a network structure that addresses these limitations by leveraging multi-level semantic information classification and fusion.Additionally,we introduce a global semantic fusion network to combat the issues of color contamination.The proposed coloring encoder accurately extracts object-level semantic information from images.To further enhance visual plausibility,we employ a self-supervised adversarial training method.We train the network structure on various datasets with varying amounts of data and evaluate its performance using the ImageNet validation set and COCO validation set.Experimental results demonstrate that our proposed algorithm can generate more realistic images compared to previous approaches,showcasing its high generalization ability.展开更多
In today’s digital era,the rapid evolution of image editing technologies has brought about a significant simplification of image manipulation.Unfortunately,this progress has also given rise to the misuse of manipulat...In today’s digital era,the rapid evolution of image editing technologies has brought about a significant simplification of image manipulation.Unfortunately,this progress has also given rise to the misuse of manipulated images across various domains.One of the pressing challenges stemming from this advancement is the increasing difficulty in discerning between unaltered and manipulated images.This paper offers a comprehensive survey of existing methodologies for detecting image tampering,shedding light on the diverse approaches employed in the field of contemporary image forensics.The methods used to identify image forgery can be broadly classified into two primary categories:classical machine learning techniques,heavily reliant on manually crafted features,and deep learning methods.Additionally,this paper explores recent developments in image forensics,placing particular emphasis on the detection of counterfeit colorization.Image colorization involves predicting colors for grayscale images,thereby enhancing their visual appeal.The advancements in colorization techniques have reached a level where distinguishing between authentic and forged images with the naked eye has become an exceptionally challenging task.This paper serves as an in-depth exploration of the intricacies of image forensics in the modern age,with a specific focus on the detection of colorization forgery,presenting a comprehensive overview of methodologies in this critical field.展开更多
An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depen...An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.展开更多
The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context,to help these people,the objective of this work is to develop an efficient text recogni...The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into speech.This system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been realized.Following this study,an acceleration of the GCM algorithm(AGCM)is proposed.The AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original method.To assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech synthesis.Our text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.展开更多
A new method for reconstructing the compressed sensing color image by solving an optimization problem based on total variation in the quaternion field is proposed, which can effectively improve the reconstructing abil...A new method for reconstructing the compressed sensing color image by solving an optimization problem based on total variation in the quaternion field is proposed, which can effectively improve the reconstructing ability of the color image. First, the color image is converted from RGB （red, green, blue） space to CMYK （cyan, magenta, yellow, black） space, which is assigned to a quaternion matrix. Meanwhile, the quaternion matrix is converted into the information of the phase and amplitude by the Euler form of the quatemion. Secondly, the phase and amplitude of the quatemion matrix are used as the smoothness constraints for the compressed sensing （CS） problem to make the reconstructing results more accurate. Finally, an iterative method based on gradient is used to solve the CS problem. Experimental results show that by considering the information of the phase and amplitude, the proposed method can achieve better performance than the existing method that treats the three components of the color image as independent parts.展开更多
A method to estimate the thickness of the sea ice of the Bohai Sea is proposed using geostationary ocean color imager （GOCI） data and then applied to the dynamic monitoring of the sea ice thickness in the Bohal Sea ...A method to estimate the thickness of the sea ice of the Bohai Sea is proposed using geostationary ocean color imager （GOCI） data and then applied to the dynamic monitoring of the sea ice thickness in the Bohal Sea during the winter of 2014 to 2015. First of all, a model is given between the GOCI shortwave broadband albedo and the reflectance of each band with high temporal resolution GOCI data. Then, the relationship model between the sea ice thickness and the GOCI shortwave broadband albedo is established and applied to the thickness extraction of the sea ice in the Bohai Sea. Finally, the sea ice thickness extraction method is tested by the results based on the MODIS data, thermodynamic empirical models （Lebedev and Zubov）, and the in situ ice thickness data. The test results not only indicated that the sea ice thickness retrieval method based on the GOCI data was a good correlation （r2〉0.66） with the sea ice thickness retrieved by the MODIS and thermodynamic empirical models, but also that the RMS is only 6.82 cm different from the thickness of the sea ice based on the GOCI and in situ data.展开更多
BACKGROUND In clinical practice,the diagnosis is sometimes difficult with contrast-enhanced ultrasound(CEUS)when the case has an atypical perfusion pattern.Color parametric imaging(CPI)is an analysis software for CEUS...BACKGROUND In clinical practice,the diagnosis is sometimes difficult with contrast-enhanced ultrasound(CEUS)when the case has an atypical perfusion pattern.Color parametric imaging(CPI)is an analysis software for CEUS with better detection of temporal differences in CEUS imaging using arbitrary colors.It measures the differences in arrival time of the contrast agent in lesions so that the perfusion features of atypical hemangioma and colorectal cancer(CRC)liver metastasis can be distinguished.AIM To evaluate the role of a novel type of CPI of CEUS in the differential diagnosis of atypical hemangioma from liver metastases in patients with a history of CRC.METHODS From January 2016 to July 2018,42 patients including 20 cases of atypical hemangioma and 22 cases of liver metastases from CRC were enrolled.These patients had a mean age of 60.5±9.3 years(range:39-75 years).All patients received ultrasound,CEUS and CPI examinations.Resident and staff radiologists independently and retrospectively reviewed CEUS and CPI images.Two sets of criteria were assigned:(1)Routine CEUS alone;and(2)CEUS and CPI.The diagnostic sensitivity,specificity,accuracy and receiver operating characteristic(ROC)curve of resident and staff radiologists were analyzed.RESULTS The following CPI features were significantly different between liver hemangioma and liver metastases analyzed by staff and resident radiologists:Peripheral nodular enhancement(65%-70.0%vs 4.5%-13.6%,P<0.001,P=0.001),mosaic/chaotic enhancement(5%-10%vs 68.2%-63.6%,P<0.001,P<0.001)and feeding artery(20%vs 59.1%-54.5%,P=0.010,P=0.021).CPI imaging offered significant improvements in detection rates compared with routine CEUS in both resident and staff groups.By resident radiologists,the specificity and accuracy of CEUS+CPI were significantly increased compared with that of CEUS(77.3%vs 45.5%,P=0.030;78.6%vs 50.0%,P=0.006).In addition,the area under the curve(AUC)of CEUS+CPI was significantly higher than that of CEUS(0.803 vs 0.757,P=0.036).By staff radiologists,accuracy was improved in CEUS+CPI(81.0%vs 54.8%,P=0.010),whereas no significant differences in specificity and sensitivity were found(P=0.144,P=0.112).The AUC of CEUS+CPI was significantly higher than that of CEUS(0.890 vs 0.825,P=0.013)by staff radiologists.CONCLUSION Compared with routine CEUS,CPI could provide specific information on the hemodynamic features of liver lesions and help to differentiate atypical hemangioma from liver metastases in patients with CRC,even for senior radiologists.展开更多
基金The National Natural Science Foundation of China(No.61572258,61173141,61271312,61232016,61272421)the Natural Science Foundation of Jiangsu Province(No.BK2012858,BK20151530)+1 种基金the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.13KJB520015)Open Fund of Jiangsu Engineering Center of Network Monitoring(No.KJR1404)
文摘In order to effectively restore color noisy images with the mixture of Gaussian noise and impulse noise,a new algorithm is proposed using the quaternion-based holistic processing idea for color images.First,a color image is represented by a pure quaternion matrix.Secondly,according to the different characteristics of the Gaussian noise and the impulse noise,an algorithm based on quaternion directional vector order statistics is used to detect the impulse noise. Finally,the quaternion optimal weights non-local means filter （QOWNLMF）for Gaussian noise removal is improved for the mixed noise removal.The detected impulse noise pixels are not considered in the calculation of weights.Experimental results on five standard images demonstrate that the proposed algorithm performs better than the commonly used robust outlyingness ratio-nonlocal means （ROR-NLM）algorithm and the optimal weights mixed filter （OWMF）.
基金supported partly by the National Basic Research Program of China (2005CB724303)the National Natural Science Foundation of China (60671062) Shanghai Leading Academic Discipline Project (B112).
文摘A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected as the features. The four features are combined together as a parameter to detect the edges of color images. Experimental results show that the method can inhibit noisy edges and facilitate the detection for weak edges. It has a better performance than conventional methods in noisy environments.
基金The work was supported in part by the Natural Science Foundation of China under Grants(Nos.61772281,61502241,61272421,61232016,61402235 and 61572258)in part by the Natural Science Foundation of Jiangsu Province,China under Grant BK20141006+1 种基金in part by the Natural Science Foundation of the Universities in Jiangsu Province under Grant 14KJB520024the PAPD fund and the CICAEET fund.
文摘In the paper,a convolutional neural network based on quaternion transformation is proposed to detect median filtering for color images.Compared with conventional convolutional neural network,color images can be processed in a holistic manner in the proposed scheme,which makes full use of the correlation between RGB channels.And due to the use of convolutional neural network,it can effectively avoid the one-sidedness of artificial features.Experimental results have shown the scheme’s improvement over the state-of-the-art scheme on the accuracy of color image median filtering detection.
基金Supported by the Fundamental Research Funds for the Central Universities (No.500421126)。
文摘Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.
基金National Natural Science Foundation of China(No.11865013)Horizontal Project of Shangrao Normal University,China(No.K8000219T)+1 种基金Industrial Science and Technology Project in Shangrao of Jiangxi Province,China(No.17A005)Doctoral Scientific Research Foundation of Shangrao Normal University,China(No.6000108)。
文摘In modern society,information is becoming increasingly interconnected through networks,and the rapid development of information technology has caused people to pay more attention to the encryption and the protection of information.Image encryption technology is a key technology for ensuring the security performance of images.We extracted single channel RGB component images from a color image using MATLAB programs,encrypted and decrypted the color images by randomly disrupting rows,columns and regions of the image.Combined with histograms and the visual judgments of encryption images,it is shown that the information of the original image cannot be obtained from the encryption image easily.The results show that the color-image encryptions with the algorithm we used have good effect and fast operation speed.Thus this algorithm has certain practical value.
基金supported by the National Natural Science Foundation of China (Grant Nos. 62405231, 62405235, and 62575229)the National Key Laboratory of Space Target Awareness (Grant Nos. STA2024KGL0203, STA2024ZCA0203, and STA-24-04-05)+3 种基金the Beijing Key Laboratory of Advanced Optical Remote Sensing Technology (Grant No. AORS202405)the China Postdoctoral Science Foundation (Grant No. 2024M762527)the Shaanxi Province High-level Innovation and Entrepreneurship Talent Program (Grant No. H02439005)the Natural Science Foundation of Shaanxi (Grant Nos. S2024-JC-JCQN-60, S2025-JCQYTS-0107, and 2025JC-QYCX-05)。
文摘In complex media scattering,multiple scattering severely degrades the optical wavefront and results in blurred images,while the spectral distortion caused by the scattering effect leads to severe color distortion.Achieving color high-resolution imaging through scattering media remains a significant challenge.Here,we propose a broadband,polarization-based method for color high-resolution imaging through scattering media.This approach enables high-resolution reconstruction by effectively separating the speckle illumination pattern from the mixed-scattering field information,leveraging polarization common-mode characteristics.Concurrently,it incorporates chromatic balance compensation to correct spectral aliasing in the scattered light field,enabling color high-resolution imaging through complex scattering media.To further optimize color distortion caused by scattering,a compensation strategy combining color constancy and white balance theory is adopted.Experimental results demonstrate that the proposed method significantly enhances both spatial resolution and color fidelity across various scattering conditions and target materials,showcasing strong adaptability and robustness.This approach provides an effective solution for achieving high-resolution color optical imaging in complex scattering environments.
文摘Abstract:Stephen Crane was an outstanding American novelist,poet,and journalist.He achieved great success in his literary works during his brief career.Crane’s most well-known work,The Red Badge of Courage,is commonly believed to be the first great novel of the American Civil War,largely because of its vivid and detailed description of the experience of warfare.This paper analyzes the images of color,animal and machine,which convey Crane’s thoughts of war:war is full of chaos,brutality,and confusion,without any romantic elements or heroism.
文摘Methods for pressure sore monitoring remain both a clinical and research challenge.Improved methodologies could assist physicians in developing prompt and effective pressure sore interventions.In this paper a technique is introduced for the assessment of pressure sores in guinea pigs,using captured color images.Sores were artificially induced,utilizing a system particularly developed for this purpose.Digital images were obtained from the suspicious region in days 3 and 7 post-pressure sore generation.Different segments of the color images were divided and labeled into three classes,based on their severity status.For quantitative analysis,a color based texture model,which is invariant against monotonic changes in illumination,is proposed.The texture model has been developed based on the local binary pattern operator.Tissue segments were classified,using the texture model and its features as inputs to a combination of neural networks.Our method is capable of discriminating tissue segments in different stages of pressure sore generation,and therefore can be a feasible tool for the early assessment of pressure sores.
基金This work is supported by the National Natural Science Foundation of China(60372060)by Changchun Science&Technology Project(03-037G10).
文摘In this article, a new way to evaluate the quality of color images is proposed, in which the properties of human vision psychology, objective fidelity, edge information, and color distortion will be combined through utilizing 3-D matrix transform. There exists color redundancy and structural similarity between three different frames of a color image, the definition of vision properties will be measured by 3-D submatrix integration transform （SIT）, in which three color components are integrated into one model and color redundancy can be exploited fully. The simulation results show that the measure index is very effective and objective in accord with vision properties.
基金Supported by the Senior University Technology Innovation Essential Project Cultivation Fund Project (Grant No 706028)the Natural Science Fund of Jiangsu Province (Grant No BK2007103)
文摘Based on the idea of second generation image coding, a novel scheme for coding still images is pre- sented.At first, an image was partitioned with a pulse-coupled neural network; and then an improved chain code and the 2D discrete cosine transform was adopted to encode the shape and the color of its edges respectively.To code its smooth and texture regions, an improved zero-trees strategy based on the 2nd generation wavelet was chosen.After that, the zero-tree chart was selected to rearrange quantified coefficients.And finally some regulations were given according to psychology of various users.Experiments under noiseless channels demonstrate that the proposed method performs better than those of the current one, such as JPEG, CMP, EZW and JPEG2000.
基金the Opening Research Foundation from LIESMARS(No.(01)0101)
文摘A method for shadow detection and compensation for color aerial images is presented.It is considered that the intensity value of each image pixel is the product of illumination function and ground object reflection,and the shadowed regions on the image are mainly caused by the short of illumination,so the information compensation for the shadowed regions should concentrate on the illumination adjustment of concerned area on the basis of the analysis of whole image.The shadow detection and compensation procedure proposed by this paper consists of four steps.
文摘The classical vector median filter (VMF) has been widely used to remove impulse noise from color images. However, since the VMF cannot identify thin lines during the denoising process, many thin lines may be removed out as noise. This serious problem can be solved by a newly proposed filter that uses a noise detector to find these thin lines and then keep them unchanged. In this new approach, the noise detection scheme applied on a current processed pixel is realized through counting the close pixels in its eight neighbor positions and the expanded window to see whether the current pixel is corrupted by impulse noise. Based on the previous outputs, our algorithm can increase the performance in detecting and canceling the impulse noise. Extensive ex- periments indicate that this approach can be used to remove the impulse noise from a color image without distorting the useful information.
基金supported by the National Natural Science Foundation of China(Grant Nos.62001249 and62375140)。
文摘Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a random color filter array(RCFA)to modulate Fourier patterns.A three-step phase-shifting technique reconstructs the Fourier spectrum,followed by an RCFA-based demosaicing algorithm to recover color images.Compared to traditional color FSI based on Bayer color filter array schemes(FSI-BCFA),our approach achieves superior separation between chrominance and luminance components in the frequency domain.Simulation results demonstrate that the FSI-RCFA method achieves a lower mean squared error(MSE),a higher peak signal-to-noise ratio(PSNR),and superior noise resistance compared to FSI-BCFA,while enabling direct single-channel pixel measurements for targeted applications such as agricultural defect detection.
基金supported by the Key Technologies R&D Program of Tianjin(Nos.24YFZCSN00030 and 24YFYSHZ00090)。
文摘Image coloring is an inherently uncertain and multimodal problem.By inputting a grayscale image into a coloring network,visually plausible colored photos can be generated.Conventional methods primarily rely on semantic information for image colorization.These methods still suffer from color contamination and semantic confusion.This is largely due to the limited capacity of convolutional neural networks to learn deep semantic information inherent in images effectively.In this paper,we propose a network structure that addresses these limitations by leveraging multi-level semantic information classification and fusion.Additionally,we introduce a global semantic fusion network to combat the issues of color contamination.The proposed coloring encoder accurately extracts object-level semantic information from images.To further enhance visual plausibility,we employ a self-supervised adversarial training method.We train the network structure on various datasets with varying amounts of data and evaluate its performance using the ImageNet validation set and COCO validation set.Experimental results demonstrate that our proposed algorithm can generate more realistic images compared to previous approaches,showcasing its high generalization ability.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1I1A3049788).
文摘In today’s digital era,the rapid evolution of image editing technologies has brought about a significant simplification of image manipulation.Unfortunately,this progress has also given rise to the misuse of manipulated images across various domains.One of the pressing challenges stemming from this advancement is the increasing difficulty in discerning between unaltered and manipulated images.This paper offers a comprehensive survey of existing methodologies for detecting image tampering,shedding light on the diverse approaches employed in the field of contemporary image forensics.The methods used to identify image forgery can be broadly classified into two primary categories:classical machine learning techniques,heavily reliant on manually crafted features,and deep learning methods.Additionally,this paper explores recent developments in image forensics,placing particular emphasis on the detection of counterfeit colorization.Image colorization involves predicting colors for grayscale images,thereby enhancing their visual appeal.The advancements in colorization techniques have reached a level where distinguishing between authentic and forged images with the naked eye has become an exceptionally challenging task.This paper serves as an in-depth exploration of the intricacies of image forensics in the modern age,with a specific focus on the detection of colorization forgery,presenting a comprehensive overview of methodologies in this critical field.
文摘An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant Number(DSR2022-RG-0114).
文摘The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into speech.This system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been realized.Following this study,an acceleration of the GCM algorithm(AGCM)is proposed.The AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original method.To assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech synthesis.Our text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.
基金The National Basic Research Program of China(973Program)(No.2011CB707904)the National Natural Science Foundation of China(No.61201344,61271312,61073138)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education(No.20110092110023,20120092120036)the Natural Science Foundation of Jiangsu Province(No.BK2012329)
文摘A new method for reconstructing the compressed sensing color image by solving an optimization problem based on total variation in the quaternion field is proposed, which can effectively improve the reconstructing ability of the color image. First, the color image is converted from RGB （red, green, blue） space to CMYK （cyan, magenta, yellow, black） space, which is assigned to a quaternion matrix. Meanwhile, the quaternion matrix is converted into the information of the phase and amplitude by the Euler form of the quatemion. Secondly, the phase and amplitude of the quatemion matrix are used as the smoothness constraints for the compressed sensing （CS） problem to make the reconstructing results more accurate. Finally, an iterative method based on gradient is used to solve the CS problem. Experimental results show that by considering the information of the phase and amplitude, the proposed method can achieve better performance than the existing method that treats the three components of the color image as independent parts.
基金The National Natural Science Foundation of China under contract No.41306193the Research and Development Special Foundation for Public Welfare Industry under of China contract No.201105016the Basic Research of First Institute of Oceanography,State Oceanic Administration under contract No.GY2014T03
文摘A method to estimate the thickness of the sea ice of the Bohai Sea is proposed using geostationary ocean color imager （GOCI） data and then applied to the dynamic monitoring of the sea ice thickness in the Bohal Sea during the winter of 2014 to 2015. First of all, a model is given between the GOCI shortwave broadband albedo and the reflectance of each band with high temporal resolution GOCI data. Then, the relationship model between the sea ice thickness and the GOCI shortwave broadband albedo is established and applied to the thickness extraction of the sea ice in the Bohai Sea. Finally, the sea ice thickness extraction method is tested by the results based on the MODIS data, thermodynamic empirical models （Lebedev and Zubov）, and the in situ ice thickness data. The test results not only indicated that the sea ice thickness retrieval method based on the GOCI data was a good correlation （r2〉0.66） with the sea ice thickness retrieved by the MODIS and thermodynamic empirical models, but also that the RMS is only 6.82 cm different from the thickness of the sea ice based on the GOCI and in situ data.
基金Supported by Capital Medical Development Program,No.2018-2-2154National Natural Science Foundation of China,No.81773286
文摘BACKGROUND In clinical practice,the diagnosis is sometimes difficult with contrast-enhanced ultrasound(CEUS)when the case has an atypical perfusion pattern.Color parametric imaging(CPI)is an analysis software for CEUS with better detection of temporal differences in CEUS imaging using arbitrary colors.It measures the differences in arrival time of the contrast agent in lesions so that the perfusion features of atypical hemangioma and colorectal cancer(CRC)liver metastasis can be distinguished.AIM To evaluate the role of a novel type of CPI of CEUS in the differential diagnosis of atypical hemangioma from liver metastases in patients with a history of CRC.METHODS From January 2016 to July 2018,42 patients including 20 cases of atypical hemangioma and 22 cases of liver metastases from CRC were enrolled.These patients had a mean age of 60.5±9.3 years(range:39-75 years).All patients received ultrasound,CEUS and CPI examinations.Resident and staff radiologists independently and retrospectively reviewed CEUS and CPI images.Two sets of criteria were assigned:(1)Routine CEUS alone;and(2)CEUS and CPI.The diagnostic sensitivity,specificity,accuracy and receiver operating characteristic(ROC)curve of resident and staff radiologists were analyzed.RESULTS The following CPI features were significantly different between liver hemangioma and liver metastases analyzed by staff and resident radiologists:Peripheral nodular enhancement(65%-70.0%vs 4.5%-13.6%,P<0.001,P=0.001),mosaic/chaotic enhancement(5%-10%vs 68.2%-63.6%,P<0.001,P<0.001)and feeding artery(20%vs 59.1%-54.5%,P=0.010,P=0.021).CPI imaging offered significant improvements in detection rates compared with routine CEUS in both resident and staff groups.By resident radiologists,the specificity and accuracy of CEUS+CPI were significantly increased compared with that of CEUS(77.3%vs 45.5%,P=0.030;78.6%vs 50.0%,P=0.006).In addition,the area under the curve(AUC)of CEUS+CPI was significantly higher than that of CEUS(0.803 vs 0.757,P=0.036).By staff radiologists,accuracy was improved in CEUS+CPI(81.0%vs 54.8%,P=0.010),whereas no significant differences in specificity and sensitivity were found(P=0.144,P=0.112).The AUC of CEUS+CPI was significantly higher than that of CEUS(0.890 vs 0.825,P=0.013)by staff radiologists.CONCLUSION Compared with routine CEUS,CPI could provide specific information on the hemodynamic features of liver lesions and help to differentiate atypical hemangioma from liver metastases in patients with CRC,even for senior radiologists.
