Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborh...Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborhood match method using genetic algorithm is used to conceal the error blocks. Experimental results show that the searching space can be greatly reduced by using genetic algorithm compared with exhaustive searching method, and good image quality is achieved. The peak signal noise ratios(PSNRs) of the restored images are increased greatly.展开更多
Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However,most of these methods cannot fully exploit spatial domain information of source images, which lead to ...Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However,most of these methods cannot fully exploit spatial domain information of source images, which lead to the degradation of image.This paper presents a fusion framework based on block-matching and 3D(BM3D) multi-scale transform. The algorithm first divides the image into different blocks and groups these 2D image blocks into 3D arrays by their similarity. Then it uses a 3D transform which consists of a 2D multi-scale and a 1D transform to transfer the arrays into transform coefficients, and then the obtained low-and high-coefficients are fused by different fusion rules. The final fused image is obtained from a series of fused 3D image block groups after the inverse transform by using an aggregation process. In the experimental part, we comparatively analyze some existing algorithms and the using of different transforms, e.g. non-subsampled Contourlet transform(NSCT), non-subsampled Shearlet transform(NSST), in the 3D transform step. Experimental results show that the proposed fusion framework can not only improve subjective visual effect, but also obtain better objective evaluation criteria than state-of-the-art methods.展开更多
In this paper, a fast image encryption algorithm is proposed, in which the shuffling and diffusion is performed simul- taneously. The cipher-text image is divided into blocks and each block has k x k pixels, while the...In this paper, a fast image encryption algorithm is proposed, in which the shuffling and diffusion is performed simul- taneously. The cipher-text image is divided into blocks and each block has k x k pixels, while the pixels of the plain-text are scanned one by one. Four logistic maps are used to generate the encryption key stream and the new place in the cipher image of plain image pixels, including the row and column of the block which the pixel belongs to and the place where the pixel would be placed in the block. After encrypting each pixel, the initial conditions of logistic maps would be changed ac- cording to the encrypted pixel＇s value; after encrypting each row of plain image, the initial condition would also be changed by the skew tent map. At last, it is illustrated that this algorithm has a faster speed, big key space, and better properties in withstanding differential attacks, statistical analysis, known plaintext, and chosen plaintext attacks.展开更多
In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of...In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix(GLCM), has been proposed and used in automatic identification systems. However,according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor(KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.展开更多
At present,underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system.However,the processed underwater terrain images have inconspicuous and few feature poi...At present,underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system.However,the processed underwater terrain images have inconspicuous and few feature points.In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed,we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block.First,the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information.The accelerated-KAZE(AKAZE)algorithm is used to combine the feature block information to match the reference image and the target image.Then,the random sample consensus(RANSAC)is applied to optimize the matching results.Finally,image fusion is performed with the global homography and the optimal seam-line method to improve the accuracy of image overlay fusion.The experimental results show that the proposed method in this paper effectively divides images into feature blocks by combining spatial information and gradient information,which not only solves the problem of stitching failure of underwater terrain images due to unobvious features,and further reduces the sensitivity to noise,but also effectively reduces the iterative calculation in the feature point matching process of the traditional method,and improves the stitching speed.Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image.展开更多
In this paper, we propose a novel block cryptographic scheme based on a spatiotemporal chaotic system and a chaotic neural network （CNN）. The employed CNN comprises a 4-neuron layer called a chaotic neuron layer （...In this paper, we propose a novel block cryptographic scheme based on a spatiotemporal chaotic system and a chaotic neural network （CNN）. The employed CNN comprises a 4-neuron layer called a chaotic neuron layer （CNL）, where the spatiotemporal chaotic system participates in generating its weight matrix and other parameters. The spatiotemporal chaotic system used in our scheme is the typical coupled map lattice （CML）, which can be easily implemented in parallel by hard- ware. A 160-bit-long binary sequence is used to generate the initial conditions of the CML. The decryption process is symmetric relative to the encryption process. Theoretical analysis and experimental results prove that the block cryptosys- tem is secure and practical, and suitable for image encryption.展开更多
The substitution box(S-box)is a fundamentally important component of symmetric key cryptosystem.An S-box is a primary source of non-linearity in modern block ciphers,and it resists the linear attack.Various approaches...The substitution box(S-box)is a fundamentally important component of symmetric key cryptosystem.An S-box is a primary source of non-linearity in modern block ciphers,and it resists the linear attack.Various approaches have been adopted to construct S-boxes.S-boxes are commonly constructed over commutative and associative algebraic structures including Galois fields,unitary commutative rings and cyclic and non-cyclic finite groups.In this paper,first a non-associative ring of order 512 is obtained by using computational techniques,and then by this ring a triplet of 8×8 S-boxes is designed.The motivation behind the designing of these S-boxes is to upsurge the robustness and broaden the key space due to non-associative and noncommutative behavior of the algebraic structure under consideration.A novel color image encryption application is anticipated in which initially these 3 S-boxes are being used to produce confusion in three layers of a standard RGB image.However,for the sake of diffusion 3D Arnold chaotic map is used in the proposed encryption scheme.A comparison with some of existing chaos and S-box dependent color image encryption schemes specs the performance results of the anticipated RGB image encryption and observed as approaching the standard prime level.展开更多
Text extraction is an important initial step in digitizing the historical documents. In this paper, we present a text extraction method for historical Tibetan document images based on block projections. The task of te...Text extraction is an important initial step in digitizing the historical documents. In this paper, we present a text extraction method for historical Tibetan document images based on block projections. The task of text extraction is considered as text area detection and location problem. The images are divided equally into blocks and the blocks are filtered by the information of the categories of connected components and corner point density. By analyzing the filtered blocks' projections, the approximate text areas can be located, and the text regions are extracted. Experiments on the dataset of historical Tibetan documents demonstrate the effectiveness of the proposed method.展开更多
Block adjustment for satellite images cannot be solved with weak convergence geometric conditions,therefore a plane block adjustment method to improve the targeting precision of images is proposed utilizing DEM as hei...Block adjustment for satellite images cannot be solved with weak convergence geometric conditions,therefore a plane block adjustment method to improve the targeting precision of images is proposed utilizing DEM as height constraint plane block adjustment method.First,a rational function model with affine transformation is selected as the mathematical model of the satellite image plane block adjustment.Second,to update the ground coordinates of tie points(TPs),the plane coordinates of TPs are only solved in the adjustment process.Elevation values are obtained by using DEM interpolation.Finally,the plane coordinates of all TPs and orientation parameters of all satellite images are solved through plane block adjustment with a few ground control points ZY-3 nadir images for two regions are tested for plane block adjustment while ZY-3 forward-nadir-back images of the same two regions are tested for stereo block adjustment.A comparison indicates that almost the same accuracy can be obtained with plane block adjustment support using a 1∶50 000 DEM and stereo block adjustment for ZY-3 images.For ZY-3 nadir images,almost no loss of plane block adjustment accuracy occurred when global DEM with 1 km grid and SRTM with 90 m grid replaced the 1∶50 000 DEM as elevation control,.Test results demonstrate the effectiveness and feasibility of the plane block adjustment method.展开更多
For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high com...For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high computational complexity,and long reconstruction time.An image compressed sensing reconstruction network based on self-attention mechanism(SAMNet)was proposed.For the compressed sampling,self-attention convolution was designed,which was conducive to capturing richer features,so that the compressed sensing measurement value retained more image structure information.For the reconstruction,a self-attention mechanism was introduced in the convolutional neural network.A reconstruction network including residual blocks,bottleneck transformer(BoTNet),and dense blocks was proposed,which strengthened the transfer of image features and reduced the amount of parameters dramatically.Under the Set5 dataset,when the measurement rates are 0.01,0.04,0.10,and 0.25,the average peak signal-to-noise ratio(PSNR)of SAMNet is improved by 1.27,1.23,0.50,and 0.15 dB,respectively,compared to the CSNet+.The running time of reconstructing a 256×256 image is reduced by 0.1473,0.1789,0.2310,and 0.2524 s compared to ReconNet.Experimental results showed that SAMNet improved the quality of reconstructed images and reduced the reconstruction time.展开更多
We propose an image retrieval method based on interest image region by asymmetrical blocking. An image is segmented into the interest region and background region on a certain rule. For the interest image regions, the...We propose an image retrieval method based on interest image region by asymmetrical blocking. An image is segmented into the interest region and background region on a certain rule. For the interest image regions, the color histogram of the uneven blocks is extracted as the color characteristic. We also collect the mean and variance value of the Gabor filtering results of background blocks as texture features of the background image. Then, the images can be retrieved by synthesizing the image color and texture features. We test our approaches by analyzing the resuits of recall and precision indicators for the Corel image database. The experiment results show that the proposed method performs effectively and accurately, which is more effective to retrieve the distant-view images, and the achieved precision increases by about 10% without loss of the retrieval call compared with some other traditional search methods.展开更多
A block difference compression algorithm based on block PSNR, Which is one of the parameters of image quality is presented for image sequence processing. This algorithm adopts classical JPEG method in intra-frame codi...A block difference compression algorithm based on block PSNR, Which is one of the parameters of image quality is presented for image sequence processing. This algorithm adopts classical JPEG method in intra-frame coding, and processes 8×8 blocks of inter-frame with different methods depending on the results from the current PSNR compared with a threshold. The calculation of threshold and the situation of error accumulation based on different thresholds as well as the structure of code stream are also presented. The advantage of this algorithm is the reduction of the large operation volume during inter-frame processing.展开更多
AIM:To propose a novel classification system for late postoperative capsular block syndrome(CBS)based on the turbidity of intracapsular fluid,and to investigate the imaging and refractive changes before and after Nd:Y...AIM:To propose a novel classification system for late postoperative capsular block syndrome(CBS)based on the turbidity of intracapsular fluid,and to investigate the imaging and refractive changes before and after Nd:YAG laser posterior capsulotomy for each subtype.METHODS:A retrospective analysis was performed on 5 eyes from 5 patients with late postoperative CBS.Patients were categorized into turbid(3 eyes)or clear(2 eyes)types based on the turbidity of intracapsular fluid.Uncorrected visual acuity(UCVA),best-corrected visual acuity(BCVA),refractive status,slit-lamp images,Pentacam Scheimpflug data,and ultrasound biomicroscopy(UBM)images were evaluated before and 1mo after successful Nd:YAG laser posterior capsulotomy.RESULTS:Nd:YAG laser posterior capsulotomy significantly improved UCVA and BCVA in all 5 late CBS eyes.Pentacam imaging:turbid intracapsular fluid showed hyperreflection between posterior capsule and intraocular lens(IOL);clear fluid showed hyporeflection.UBM:posterior capsule was clear in turbid type but poorly defined in clear type.Capsulotomy increased aqueous depth only in clear type.Refractive changes:turbid fluid induced myopic shift.Clear type myopic shift was due to anterior IOL displacement and clear fluid-induced concave lens effect.CONCLUSION:Nd:YAG laser posterior capsulotomy is effective for late CBS.Turbid and clear late CBS types differ in imaging(Pentacam/UBM)and refractive mechanisms,supporting the proposed classification’s clinical value.展开更多
To compress screen image sequence in real-time remote and interactive applications,a novel compression method is proposed.The proposed method is named as CABHG.CABHG employs hybrid coding schemes that consist of intra...To compress screen image sequence in real-time remote and interactive applications,a novel compression method is proposed.The proposed method is named as CABHG.CABHG employs hybrid coding schemes that consist of intra-frame and inter-frame coding modes.The intra-frame coding is a rate-distortion optimized adaptive block size that can be also used for the compression of a single screen image.The inter-frame coding utilizes hierarchical group of pictures(GOP) structure to improve system performance during random accesses and fast-backward scans.Experimental results demonstrate that the proposed CABHG method has approximately 47%-48% higher compression ratio and 46%-53% lower CPU utilization than professional screen image sequence codecs such as TechSmith Ensharpen codec and Sorenson 3 codec.Compared with general video codecs such as H.264 codec,XviD MPEG-4 codec and Apple's Animation codec,CABHG also shows 87%-88% higher compression ratio and 64%-81% lower CPU utilization than these general video codecs.展开更多
Copy-move forgery is the most common type of digital image manipulation,in which the content from the same image is used to forge it.Such manipulations are performed to hide the desired information.Therefore,forgery d...Copy-move forgery is the most common type of digital image manipulation,in which the content from the same image is used to forge it.Such manipulations are performed to hide the desired information.Therefore,forgery detection methods are required to identify forged areas.We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern(LTrP)features to detect the single and multiple copy-move attacks from the images.The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations.It also uses discrete wavelet transform(DWT)for dimension reduction.The obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its neighborhoods.Finally,Jeffreys and Matusita distance is used for similarity measurement.For the evaluation of the results,three datasets are used,namely MICC-F220,MICC-F2000,and CoMoFoD.Both the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.展开更多
This paper proposes an electronic image stabilization algorithm based on efficient block matching on the plane. This algorithm uses a hexagonal search algorithm, and uses the bit-planes to estimate and compensate for ...This paper proposes an electronic image stabilization algorithm based on efficient block matching on the plane. This algorithm uses a hexagonal search algorithm, and uses the bit-planes to estimate and compensate for the translational motion between video sequences at the same time;As for the rotary motion vector generated in the video sequences, in order to highlight the intensity change of the image sequence, the algorithm firstly conducts Laplace transform for the reference frame, then select a number of characteristics at the image edge to make block matching with the current frame, calculate and compensate for the rotational movement that may exist finally. Through theoretical analysis and simula-tion, we prove that, as for a mixed translational and rotational motion video sequences, the proposed algorithm can reduce required time for block matching computation ,while improving the accuracy of the electronic image stabilization.展开更多
Due to coarse quantization, block-based discrete cosine transform(BDCT) compression methods usually suffer from visible blocking artifacts at the block boundaries. A novel efficient de-blocking method in DCT domain is...Due to coarse quantization, block-based discrete cosine transform(BDCT) compression methods usually suffer from visible blocking artifacts at the block boundaries. A novel efficient de-blocking method in DCT domain is proposed. A specific criterion for edge detection is given, one-dimensional DCT is applied on each row of the adjacent blocks and the shifted block in smooth region, and the transform coefficients of the shifted block are modified by weighting the average of three coefficients of the block. Mean square difference of slope criterion is used to judge the efficiency of the proposed algorithm. Simulation results show that the new method not only obtains satisfactory image quality, but also maintains high frequency information.展开更多
The identification of objects in binary images is a fundamental task in image analysis and pattern recognition tasks. The Euler number of a binary image is an important topological measure which is used as a feature i...The identification of objects in binary images is a fundamental task in image analysis and pattern recognition tasks. The Euler number of a binary image is an important topological measure which is used as a feature in image analysis. In this paper, a very fast algorithm for the detection and localization of the objects and the computation of the Euler number of a binary image is proposed. The proposed algorithm operates in one scan of the image and is based on the Image Block Representation (IBR) scheme. The proposed algorithm is more efficient than conventional pixel based algorithms in terms of execution speed and representation of the extracted information.展开更多
Image denoising has become one of the major forms of image enhancement methods that form the basis of image processing. Due to the inconsistencies in the machinery producing these signals, medical images tend to requi...Image denoising has become one of the major forms of image enhancement methods that form the basis of image processing. Due to the inconsistencies in the machinery producing these signals, medical images tend to require these techniques. In real time, images do not contain a single noise, and instead they contain multiple types of noise distributions in several indistinct regions. This paper presents an image denoising method that uses Metaheuristics to perform noise identification. Adaptive block selection is used to identify and correct the noise contained in these blocks. Though the system uses a block selection scheme, modifications are performed on pixel- to-pixel basis and not on the entire blocks;hence the image accuracy is preserved. PSO is used to identify the noise distribution, and appropriate noise correction techniques are applied to denoise the images. Experiments were conducted using salt and pepper noise, Gaussian noise and a combination of both the noise in the same image. It was observed that the proposed method performed effectively on noise levels up-to 0.5 and was able to produce results with PSNR values ranging from 20 to 30 in most of the cases. Excellent reduction rates were observed on salt and pepper noise and moderate reduction rates were observed on Gaussian noise. Experimental results show that our proposed system has a wide range of applicability in any domain specific image denoising scenario, such as medical imaging, mammogram etc.展开更多
Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summariza...Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.展开更多
文摘Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborhood match method using genetic algorithm is used to conceal the error blocks. Experimental results show that the searching space can be greatly reduced by using genetic algorithm compared with exhaustive searching method, and good image quality is achieved. The peak signal noise ratios(PSNRs) of the restored images are increased greatly.
基金supported by the National Natural Science Foundation of China(6157206361401308)+6 种基金the Fundamental Research Funds for the Central Universities(2016YJS039)the Natural Science Foundation of Hebei Province(F2016201142F2016201187)the Natural Social Foundation of Hebei Province(HB15TQ015)the Science Research Project of Hebei Province(QN2016085ZC2016040)the Natural Science Foundation of Hebei University(2014-303)
文摘Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However,most of these methods cannot fully exploit spatial domain information of source images, which lead to the degradation of image.This paper presents a fusion framework based on block-matching and 3D(BM3D) multi-scale transform. The algorithm first divides the image into different blocks and groups these 2D image blocks into 3D arrays by their similarity. Then it uses a 3D transform which consists of a 2D multi-scale and a 1D transform to transfer the arrays into transform coefficients, and then the obtained low-and high-coefficients are fused by different fusion rules. The final fused image is obtained from a series of fused 3D image block groups after the inverse transform by using an aggregation process. In the experimental part, we comparatively analyze some existing algorithms and the using of different transforms, e.g. non-subsampled Contourlet transform(NSCT), non-subsampled Shearlet transform(NSST), in the 3D transform step. Experimental results show that the proposed fusion framework can not only improve subjective visual effect, but also obtain better objective evaluation criteria than state-of-the-art methods.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61370145,61173183,and 60973152)the Doctoral Program Foundation of Institution of Higher Education of China(Grant No.20070141014)+2 种基金the Program for Liaoning Excellent Talents in University,China(Grant No.LR2012003)the National Natural Science Foundation of Liaoning Province,China(Grant No.20082165)the Fundamental Research Funds for the Central Universities,China(Grant No.DUT12JB06)
文摘In this paper, a fast image encryption algorithm is proposed, in which the shuffling and diffusion is performed simul- taneously. The cipher-text image is divided into blocks and each block has k x k pixels, while the pixels of the plain-text are scanned one by one. Four logistic maps are used to generate the encryption key stream and the new place in the cipher image of plain image pixels, including the row and column of the block which the pixel belongs to and the place where the pixel would be placed in the block. After encrypting each pixel, the initial conditions of logistic maps would be changed ac- cording to the encrypted pixel＇s value; after encrypting each row of plain image, the initial condition would also be changed by the skew tent map. At last, it is illustrated that this algorithm has a faster speed, big key space, and better properties in withstanding differential attacks, statistical analysis, known plaintext, and chosen plaintext attacks.
基金the Yunnan Applied Basic Research Projects(No.2016FD039)the Talent Cultivation Project in Yunnan Province(No.KKSY201503063)
文摘In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix(GLCM), has been proposed and used in automatic identification systems. However,according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor(KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.
基金This research was funded by College Student Innovation and Entrepreneurship Training Program,Grant Number 2021055Z and S202110082031the Special Project for Cultivating Scientific and Technological Innovation Ability of College and Middle School Students in Hebei Province,Grant Number 2021H011404.
文摘At present,underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system.However,the processed underwater terrain images have inconspicuous and few feature points.In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed,we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block.First,the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information.The accelerated-KAZE(AKAZE)algorithm is used to combine the feature block information to match the reference image and the target image.Then,the random sample consensus(RANSAC)is applied to optimize the matching results.Finally,image fusion is performed with the global homography and the optimal seam-line method to improve the accuracy of image overlay fusion.The experimental results show that the proposed method in this paper effectively divides images into feature blocks by combining spatial information and gradient information,which not only solves the problem of stitching failure of underwater terrain images due to unobvious features,and further reduces the sensitivity to noise,but also effectively reduces the iterative calculation in the feature point matching process of the traditional method,and improves the stitching speed.Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 61173183, 60973152, and 60573172)the Doctoral Program Foundation of Institution of Higher Education of China (Grant No. 20070141014)+2 种基金the Program for Excellent Talents in Universities of Liaoning Province, China (Grant No. LR2012003)the Natural Science Foundation of Liaoning Province, China (Grant No. 20082165)the Fundamental Research Funds for the Central Universities of China (Grant No. DUT12JB06)
文摘In this paper, we propose a novel block cryptographic scheme based on a spatiotemporal chaotic system and a chaotic neural network （CNN）. The employed CNN comprises a 4-neuron layer called a chaotic neuron layer （CNL）, where the spatiotemporal chaotic system participates in generating its weight matrix and other parameters. The spatiotemporal chaotic system used in our scheme is the typical coupled map lattice （CML）, which can be easily implemented in parallel by hard- ware. A 160-bit-long binary sequence is used to generate the initial conditions of the CML. The decryption process is symmetric relative to the encryption process. Theoretical analysis and experimental results prove that the block cryptosys- tem is secure and practical, and suitable for image encryption.
文摘The substitution box(S-box)is a fundamentally important component of symmetric key cryptosystem.An S-box is a primary source of non-linearity in modern block ciphers,and it resists the linear attack.Various approaches have been adopted to construct S-boxes.S-boxes are commonly constructed over commutative and associative algebraic structures including Galois fields,unitary commutative rings and cyclic and non-cyclic finite groups.In this paper,first a non-associative ring of order 512 is obtained by using computational techniques,and then by this ring a triplet of 8×8 S-boxes is designed.The motivation behind the designing of these S-boxes is to upsurge the robustness and broaden the key space due to non-associative and noncommutative behavior of the algebraic structure under consideration.A novel color image encryption application is anticipated in which initially these 3 S-boxes are being used to produce confusion in three layers of a standard RGB image.However,for the sake of diffusion 3D Arnold chaotic map is used in the proposed encryption scheme.A comparison with some of existing chaos and S-box dependent color image encryption schemes specs the performance results of the anticipated RGB image encryption and observed as approaching the standard prime level.
基金supported by the Innovation Platform Construction of Qinghai Province(No.2016-ZJ-Y04)the Basic Research Program of Qinghai Province(No.2016-ZJ-740)
文摘Text extraction is an important initial step in digitizing the historical documents. In this paper, we present a text extraction method for historical Tibetan document images based on block projections. The task of text extraction is considered as text area detection and location problem. The images are divided equally into blocks and the blocks are filtered by the information of the categories of connected components and corner point density. By analyzing the filtered blocks' projections, the approximate text areas can be located, and the text regions are extracted. Experiments on the dataset of historical Tibetan documents demonstrate the effectiveness of the proposed method.
文摘Block adjustment for satellite images cannot be solved with weak convergence geometric conditions,therefore a plane block adjustment method to improve the targeting precision of images is proposed utilizing DEM as height constraint plane block adjustment method.First,a rational function model with affine transformation is selected as the mathematical model of the satellite image plane block adjustment.Second,to update the ground coordinates of tie points(TPs),the plane coordinates of TPs are only solved in the adjustment process.Elevation values are obtained by using DEM interpolation.Finally,the plane coordinates of all TPs and orientation parameters of all satellite images are solved through plane block adjustment with a few ground control points ZY-3 nadir images for two regions are tested for plane block adjustment while ZY-3 forward-nadir-back images of the same two regions are tested for stereo block adjustment.A comparison indicates that almost the same accuracy can be obtained with plane block adjustment support using a 1∶50 000 DEM and stereo block adjustment for ZY-3 images.For ZY-3 nadir images,almost no loss of plane block adjustment accuracy occurred when global DEM with 1 km grid and SRTM with 90 m grid replaced the 1∶50 000 DEM as elevation control,.Test results demonstrate the effectiveness and feasibility of the plane block adjustment method.
基金supported by National Natural Science Foundation of China(Nos.61261016,61661025)Science and Technology Plan of Gansu Province(No.20JR10RA273).
文摘For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high computational complexity,and long reconstruction time.An image compressed sensing reconstruction network based on self-attention mechanism(SAMNet)was proposed.For the compressed sampling,self-attention convolution was designed,which was conducive to capturing richer features,so that the compressed sensing measurement value retained more image structure information.For the reconstruction,a self-attention mechanism was introduced in the convolutional neural network.A reconstruction network including residual blocks,bottleneck transformer(BoTNet),and dense blocks was proposed,which strengthened the transfer of image features and reduced the amount of parameters dramatically.Under the Set5 dataset,when the measurement rates are 0.01,0.04,0.10,and 0.25,the average peak signal-to-noise ratio(PSNR)of SAMNet is improved by 1.27,1.23,0.50,and 0.15 dB,respectively,compared to the CSNet+.The running time of reconstructing a 256×256 image is reduced by 0.1473,0.1789,0.2310,and 0.2524 s compared to ReconNet.Experimental results showed that SAMNet improved the quality of reconstructed images and reduced the reconstruction time.
基金Supported by the National Natural Science Foundation of China (50803016)
文摘We propose an image retrieval method based on interest image region by asymmetrical blocking. An image is segmented into the interest region and background region on a certain rule. For the interest image regions, the color histogram of the uneven blocks is extracted as the color characteristic. We also collect the mean and variance value of the Gabor filtering results of background blocks as texture features of the background image. Then, the images can be retrieved by synthesizing the image color and texture features. We test our approaches by analyzing the resuits of recall and precision indicators for the Corel image database. The experiment results show that the proposed method performs effectively and accurately, which is more effective to retrieve the distant-view images, and the achieved precision increases by about 10% without loss of the retrieval call compared with some other traditional search methods.
文摘A block difference compression algorithm based on block PSNR, Which is one of the parameters of image quality is presented for image sequence processing. This algorithm adopts classical JPEG method in intra-frame coding, and processes 8×8 blocks of inter-frame with different methods depending on the results from the current PSNR compared with a threshold. The calculation of threshold and the situation of error accumulation based on different thresholds as well as the structure of code stream are also presented. The advantage of this algorithm is the reduction of the large operation volume during inter-frame processing.
基金Supported by the Research and Development Program of Shaanxi Province(No.2021SF-161)the Medical Research Project of Xi’an Science and Technology Action Plan[No.2019114613YX001SF041(1)]。
文摘AIM:To propose a novel classification system for late postoperative capsular block syndrome(CBS)based on the turbidity of intracapsular fluid,and to investigate the imaging and refractive changes before and after Nd:YAG laser posterior capsulotomy for each subtype.METHODS:A retrospective analysis was performed on 5 eyes from 5 patients with late postoperative CBS.Patients were categorized into turbid(3 eyes)or clear(2 eyes)types based on the turbidity of intracapsular fluid.Uncorrected visual acuity(UCVA),best-corrected visual acuity(BCVA),refractive status,slit-lamp images,Pentacam Scheimpflug data,and ultrasound biomicroscopy(UBM)images were evaluated before and 1mo after successful Nd:YAG laser posterior capsulotomy.RESULTS:Nd:YAG laser posterior capsulotomy significantly improved UCVA and BCVA in all 5 late CBS eyes.Pentacam imaging:turbid intracapsular fluid showed hyperreflection between posterior capsule and intraocular lens(IOL);clear fluid showed hyporeflection.UBM:posterior capsule was clear in turbid type but poorly defined in clear type.Capsulotomy increased aqueous depth only in clear type.Refractive changes:turbid fluid induced myopic shift.Clear type myopic shift was due to anterior IOL displacement and clear fluid-induced concave lens effect.CONCLUSION:Nd:YAG laser posterior capsulotomy is effective for late CBS.Turbid and clear late CBS types differ in imaging(Pentacam/UBM)and refractive mechanisms,supporting the proposed classification’s clinical value.
基金Project(60873230) supported by the National Natural Science Foundation of China
文摘To compress screen image sequence in real-time remote and interactive applications,a novel compression method is proposed.The proposed method is named as CABHG.CABHG employs hybrid coding schemes that consist of intra-frame and inter-frame coding modes.The intra-frame coding is a rate-distortion optimized adaptive block size that can be also used for the compression of a single screen image.The inter-frame coding utilizes hierarchical group of pictures(GOP) structure to improve system performance during random accesses and fast-backward scans.Experimental results demonstrate that the proposed CABHG method has approximately 47%-48% higher compression ratio and 46%-53% lower CPU utilization than professional screen image sequence codecs such as TechSmith Ensharpen codec and Sorenson 3 codec.Compared with general video codecs such as H.264 codec,XviD MPEG-4 codec and Apple's Animation codec,CABHG also shows 87%-88% higher compression ratio and 64%-81% lower CPU utilization than these general video codecs.
文摘Copy-move forgery is the most common type of digital image manipulation,in which the content from the same image is used to forge it.Such manipulations are performed to hide the desired information.Therefore,forgery detection methods are required to identify forged areas.We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern(LTrP)features to detect the single and multiple copy-move attacks from the images.The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations.It also uses discrete wavelet transform(DWT)for dimension reduction.The obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its neighborhoods.Finally,Jeffreys and Matusita distance is used for similarity measurement.For the evaluation of the results,three datasets are used,namely MICC-F220,MICC-F2000,and CoMoFoD.Both the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.
文摘This paper proposes an electronic image stabilization algorithm based on efficient block matching on the plane. This algorithm uses a hexagonal search algorithm, and uses the bit-planes to estimate and compensate for the translational motion between video sequences at the same time;As for the rotary motion vector generated in the video sequences, in order to highlight the intensity change of the image sequence, the algorithm firstly conducts Laplace transform for the reference frame, then select a number of characteristics at the image edge to make block matching with the current frame, calculate and compensate for the rotational movement that may exist finally. Through theoretical analysis and simula-tion, we prove that, as for a mixed translational and rotational motion video sequences, the proposed algorithm can reduce required time for block matching computation ,while improving the accuracy of the electronic image stabilization.
基金Science and Technology Project of Guangdong Province(2006A10201003) 2005 Jinan University StartupProject(51205067) Soft Science Project of Guangdong Province(2006B70103011)
文摘Due to coarse quantization, block-based discrete cosine transform(BDCT) compression methods usually suffer from visible blocking artifacts at the block boundaries. A novel efficient de-blocking method in DCT domain is proposed. A specific criterion for edge detection is given, one-dimensional DCT is applied on each row of the adjacent blocks and the shifted block in smooth region, and the transform coefficients of the shifted block are modified by weighting the average of three coefficients of the block. Mean square difference of slope criterion is used to judge the efficiency of the proposed algorithm. Simulation results show that the new method not only obtains satisfactory image quality, but also maintains high frequency information.
文摘The identification of objects in binary images is a fundamental task in image analysis and pattern recognition tasks. The Euler number of a binary image is an important topological measure which is used as a feature in image analysis. In this paper, a very fast algorithm for the detection and localization of the objects and the computation of the Euler number of a binary image is proposed. The proposed algorithm operates in one scan of the image and is based on the Image Block Representation (IBR) scheme. The proposed algorithm is more efficient than conventional pixel based algorithms in terms of execution speed and representation of the extracted information.
文摘Image denoising has become one of the major forms of image enhancement methods that form the basis of image processing. Due to the inconsistencies in the machinery producing these signals, medical images tend to require these techniques. In real time, images do not contain a single noise, and instead they contain multiple types of noise distributions in several indistinct regions. This paper presents an image denoising method that uses Metaheuristics to perform noise identification. Adaptive block selection is used to identify and correct the noise contained in these blocks. Though the system uses a block selection scheme, modifications are performed on pixel- to-pixel basis and not on the entire blocks;hence the image accuracy is preserved. PSO is used to identify the noise distribution, and appropriate noise correction techniques are applied to denoise the images. Experiments were conducted using salt and pepper noise, Gaussian noise and a combination of both the noise in the same image. It was observed that the proposed method performed effectively on noise levels up-to 0.5 and was able to produce results with PSNR values ranging from 20 to 30 in most of the cases. Excellent reduction rates were observed on salt and pepper noise and moderate reduction rates were observed on Gaussian noise. Experimental results show that our proposed system has a wide range of applicability in any domain specific image denoising scenario, such as medical imaging, mammogram etc.
基金The National Natural Science Foundation of China(No.61133012)the Humanity and Social Science Foundation of the Ministry of Education(No.12YJCZH274)+1 种基金the Humanity and Social Science Foundation of Jiangxi Province(No.XW1502,TQ1503)the Science and Technology Project of Jiangxi Science and Technology Department(No.20121BBG70050,20142BBG70011)
文摘Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.
