To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achiev...To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achieves high-quality AFM imaging via line-by-line corrections for each distorted profile along the fast axis.The key to this line-by-line correction is to accurately simulate the profile distortion of each scanning row.Therefore,a data preprocessing approach is first developed to roughly filter out most of the height data that impairs the accuracy of distortion modeling.This process is implemented through an internal double-screening mechanism.A line-fitting method is adopted to preliminarily screen out the obvious specimens.Lifting wavelet analysis is then carried out to identify the base parts that are mistakenly filtered out as specimens so as to preserve most of the base profiles and provide a good basis for further distortion modeling.Next,an iterative thresholding algorithm is developed to precisely simulate the profile distortion.By utilizing the roughly screened base profile,the optimal threshold,which is used to screen out the pure bases suitable for distortion modeling,is determined through iteration with a specified error rule.On this basis,the profile distortion is accurately modeled through line fitting on the finely screened base data,and the correction is implemented by subtracting the modeling result from the distorted profile.Finally,the effectiveness of the proposed method is verified through experiments and applications.展开更多
The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- ...The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- sents a novel signal processing scheme, adaptive morpho- logical update lifting wavelet （AMULW）, for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration sig- nals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective roiling element bearings.展开更多
The Savitsky-Golay filter isa smoothing filter based on polynomial regression.Itemploys the regression fitting capacity to improve the smoothing results.But Savit-sky-Golay filter uses a fix sized window.It has the sa...The Savitsky-Golay filter isa smoothing filter based on polynomial regression.Itemploys the regression fitting capacity to improve the smoothing results.But Savit-sky-Golay filter uses a fix sized window.It has the same shortage of Window FourierTransform.Wavelet mutiresolution analysis may deal with this problem.In this paper,tak-ing advantage of Savitsky-Golay filter's fitting ability and the wavelet transform's multiscaleanalysis ability,we developed a new lifting transform via Savitsky-Golay smoothing filteras the lifting predictor,and then processed the signals comparing with the ordinary Savit-sky-Golay Smoothing method.We useed the new lifting in noisy heavy sine denoising.Thenew transform obviously has better denoise ability than ordinary Savitsky-Golay smooth-ing method.At the same time singular points are perfectly retained in the denoised signal.Singularity analysis,multiscale interpolation,estimation,chemical data smoothing andother potential signal processing utility of this new lifting transform are in prospect.展开更多
Field spectrum pretreatment experiments were carried out, and denoising numerical experiment via lifting wavelet transform （LWT） was designed, and several famous test signals including blocks, bumps, heavy sine and ...Field spectrum pretreatment experiments were carried out, and denoising numerical experiment via lifting wavelet transform （LWT） was designed, and several famous test signals including blocks, bumps, heavy sine and doppler were processed via Lw＇r in these experiment. And the field spectrum was processed via Lw＇r. Experiments proved that SNRG-tO-SNRN curves have similar feature and they all have a peak. And SNRG of almost all employed wavelets have higher value with SNRN between 0 and 20 dB. When signal is at high SNR, the SNRG is very little, and the MSED of denoised signal became little by little. LWT is more suite to denoise the low SNR or heavy noise contaminated signals. Bior4.4 have wider SNRN interval for denoising comparing with other five wavelets, includ- ing haar, db6, sym6, bior2.2 and bior3.3. Original field spectrum is processed by 3 stage liftings based on bior4.4 to denoise the trivial noise-contaminated regions. On processing the water band signal, logarithm transform is firstly taken. And then the spectrum is denoised via LWT based on bior4.4. The results show that an excellent denoised spectrum can be get, especially between 350 nm and 1 800 nm, and between 1 960 nm to 2 500 nm. While there is still a bump around 1 900 nm, this maybe due to the spectrum machine＇s limited precision.展开更多
In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuse...In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuses on deep learning-based approaches to improve the quality of watermarked images,but they have some flaws.To overcome this,the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital image.Recently,quantum logistic maps,which combine the concept of quantum computing with traditional techniques,have been considered a niche and promising area of research that has attracted researchers’attention to further research in digital watermarking.This research uses the chaotic behaviour of the quantum logistic map with Rivest–Shamir–Adleman(RSA)and Secure Hash(SHA-3)algorithms for a robust watermark embedding process,where a watermark is embedded into the host image.This way,the quantum chaos method not only helps limit the chance of tampering with the image content through reverse engineering but also assists in maintaining a high level of imperceptibility and strong robustness with efficient extraction or detection of watermark images.Lifting Wavelet Transformation(LWT)is a potential and computationally efficient version of traditional Discrete Wavelet Transform(DWT)where the host image is divided into four sub-bands to offer a multi-resolution view of an image with greater flexibility in watermarking methodologies.Furthermore,considering the robustness against attacks,a pre-trained Residual Neural Network(ResNet-50),a convolutional neural network with 50 layers deep,is used to better learn the complex features and efficiently extract the watermark from the image.By integrating RSA and SHA-3 algorithms,the proposed model demonstrates improved imperceptibility,robustness,and accuracy in watermark extraction compared to traditional methods.It achieves a Peak Signal-to-Noise Ratio(PSNR)of 49.83%,a Structural Similarity Index Measure(SSIM)of 0.98,and a Number of Pixels Change Rate(NPCR)of 99.79%,respectively.These results reflect the model’s effectiveness in delivering superior quality and security.Consequently,our proposed approach offers accurate results,exceptional invisibility,and enhanced robustness compared to the existing digital image watermarking techniques.展开更多
We present a novel quantization-based digital audio walermarking scheme inwavelet domain. By quantizing a host audio's wavelet coefficients (Integer Lifting WaveletTransform) and utilizing the characteristics of h...We present a novel quantization-based digital audio walermarking scheme inwavelet domain. By quantizing a host audio's wavelet coefficients (Integer Lifting WaveletTransform) and utilizing the characteristics of human auditory system (HAS), the grayimage isembedded using our watermarking method. Experimental results show that the proposed watermarkingscheme is inaudible and robust against various signal processing such as noising adding, lossycompression, low pass filtering, re-sampling, and re-quantifying.展开更多
Technological advancement of measurement systems has enhanced the accuracy of power quality assessment by using a combination of measured information. This paper proposes a novel approach for estimating power quality ...Technological advancement of measurement systems has enhanced the accuracy of power quality assessment by using a combination of measured information. This paper proposes a novel approach for estimating power quality based on information fusion technique of Dempster-Shafer(D-S) evidence theory. First, in order to accurately extract transient features regarding power quality indexes, wavelet packet transform and lifting wavelet transform are proposed to detect various disturbance signals measurement. By using many kinds of transformed transient indexes and steady state indexes, a novel reliability distribution function is constructed,and synthesized assessment index of power quality is drafted based on information fusion technique of D-S evidence theory. Finally,the simulation results prove that D-S evidence theory is a more effective means for evaluating the power quality.展开更多
Traditional image encryption algorithms transform a plain image into a noise-like image.To lower the chances for the encrypted image being detected by the attacker during the image transmission,a visually meaningful i...Traditional image encryption algorithms transform a plain image into a noise-like image.To lower the chances for the encrypted image being detected by the attacker during the image transmission,a visually meaningful image encryption scheme is suggested to hide the encrypted image using another carrier image.This paper proposes a visually meaningful encrypted image algorithm that hides a secret image and a digital signature which provides authenticity and confidentiality.The recovered digital signature is used for the purpose of identity authentication while the secret image is encrypted to protect its confidentiality.Least Significant Bit(LSB)method to embed signature on the encrypted image and Lifting Wavelet Transform(LWT)to generate a visually meaningful encrypted image are designed.The proposed algorithm has a keyspace of 139.5-bit,a Normalized Correlation(NC)value of 0.9998 which is closer to 1 and a Peak Signal to Noise Ratio(PSNR)with a value greater than 50 dB.Different analyses are also performed on the proposed algorithm using different images.The experimental results show that the proposed scheme is with high key sensitivity and strong robustness against pepper and salt attack and cropping attack.Moreover,the histogram analysis shows that the original carrier image and the final visual image are very similar.展开更多
Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the web.Usually, the copyright information is embossed over the image in the form ofa l...Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the web.Usually, the copyright information is embossed over the image in the form ofa logo at the corner or diagonal text in the background. However, this formof visible watermarking is not suitable for a large class of applications. In allsuch cases, a hidden watermark is embedded inside the original image as proofof ownership. A large number of techniques and algorithms are proposedby researchers for invisible watermarking. In this paper, we focus on issuesthat are critical for security aspects in the most common domains like digitalphotography copyrighting, online image stores, etc. The requirements of thisclass of application include robustness (resistance to attack), blindness (directextraction without original image), high embedding capacity, high Peak Signalto Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM). Mostof these requirements are conflicting, which means that an attempt to maximizeone requirement harms the other. In this paper, a blind type of imagewatermarking scheme is proposed using Lifting Wavelet Transform (LWT)as the baseline. Using this technique, custom binary watermarks in the formof a binary string can be embedded. Hu’s Invariant moments’ coefficientsare used as a key to extract the watermark. A Stochastic variant of theFirefly algorithm (FA) is used for the optimization of the technique. Undera prespecified size of embedding data, high PSNR and SSIM are obtainedusing the Stochastic Gradient variant of the Firefly technique. The simulationis done using Matrix Laboratory (MATLAB) tool and it is shown that theproposed technique outperforms the benchmark techniques of watermarkingconsidering PSNR and SSIM as quality metrics.展开更多
In this paper, we present a new technique for mammogram enhancement using fast dyadic wavelet transform （FDyWT） based on lifted spline dyadic wavelets and normalized Tsallis entropy. First, a mammogram image is deco...In this paper, we present a new technique for mammogram enhancement using fast dyadic wavelet transform （FDyWT） based on lifted spline dyadic wavelets and normalized Tsallis entropy. First, a mammogram image is decom- posed into a multiscale hierarchy of low-subband and high-subband images using FDyWT. Then noise is suppressed using normalized Tsallis entropy of the local variance of the modulus of oriented high-subband images. After that, the wavelet coefficients of high-subbands are modified using a non-linear operator and finally the low-subband image at the first scale is modified with power law transformation to suppress background. Though FDyWT is shift-invariant and has better poten- tial for detecting singularities like edges, its performance depends on the choice of dyadic wavclcts. On the other hand, the nulnber of vanishing moments is an important characteristic of dyadic wavelets for singularity analysis because it provides an upper bound measurement for singularity characterization. Using lifting dyadic schemes, we construct lifted spline dyadic wavelets of different degrees with increased number of vanishing moments. We also examine the effect of these wavelets on mammogram enhancement. The method is tested on mammogram images, taken from MIAS （Mammographic Image Analysis Society） database, having various background tissue types and containing different abnormalities. The comparison with tile state-of-the-art contrast enhancement methods reveals that the proposed method performs better and the difference is statistically significant.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
基金supported by the National Natural Science Foundation of China under Grant No.21933006.
文摘To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achieves high-quality AFM imaging via line-by-line corrections for each distorted profile along the fast axis.The key to this line-by-line correction is to accurately simulate the profile distortion of each scanning row.Therefore,a data preprocessing approach is first developed to roughly filter out most of the height data that impairs the accuracy of distortion modeling.This process is implemented through an internal double-screening mechanism.A line-fitting method is adopted to preliminarily screen out the obvious specimens.Lifting wavelet analysis is then carried out to identify the base parts that are mistakenly filtered out as specimens so as to preserve most of the base profiles and provide a good basis for further distortion modeling.Next,an iterative thresholding algorithm is developed to precisely simulate the profile distortion.By utilizing the roughly screened base profile,the optimal threshold,which is used to screen out the pure bases suitable for distortion modeling,is determined through iteration with a specified error rule.On this basis,the profile distortion is accurately modeled through line fitting on the finely screened base data,and the correction is implemented by subtracting the modeling result from the distorted profile.Finally,the effectiveness of the proposed method is verified through experiments and applications.
基金Supported by National Natural Science Foundation of China(51705431,51375078)Natural Sciences and Engineering Research Council of Canada(RGPIN-2015-04897)
文摘The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- sents a novel signal processing scheme, adaptive morpho- logical update lifting wavelet （AMULW）, for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration sig- nals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective roiling element bearings.
基金Supported by Land and Resource Ministry of China(30302408-3)
文摘The Savitsky-Golay filter isa smoothing filter based on polynomial regression.Itemploys the regression fitting capacity to improve the smoothing results.But Savit-sky-Golay filter uses a fix sized window.It has the same shortage of Window FourierTransform.Wavelet mutiresolution analysis may deal with this problem.In this paper,tak-ing advantage of Savitsky-Golay filter's fitting ability and the wavelet transform's multiscaleanalysis ability,we developed a new lifting transform via Savitsky-Golay smoothing filteras the lifting predictor,and then processed the signals comparing with the ordinary Savit-sky-Golay Smoothing method.We useed the new lifting in noisy heavy sine denoising.Thenew transform obviously has better denoise ability than ordinary Savitsky-Golay smooth-ing method.At the same time singular points are perfectly retained in the denoised signal.Singularity analysis,multiscale interpolation,estimation,chemical data smoothing andother potential signal processing utility of this new lifting transform are in prospect.
文摘Field spectrum pretreatment experiments were carried out, and denoising numerical experiment via lifting wavelet transform （LWT） was designed, and several famous test signals including blocks, bumps, heavy sine and doppler were processed via Lw＇r in these experiment. And the field spectrum was processed via Lw＇r. Experiments proved that SNRG-tO-SNRN curves have similar feature and they all have a peak. And SNRG of almost all employed wavelets have higher value with SNRN between 0 and 20 dB. When signal is at high SNR, the SNRG is very little, and the MSED of denoised signal became little by little. LWT is more suite to denoise the low SNR or heavy noise contaminated signals. Bior4.4 have wider SNRN interval for denoising comparing with other five wavelets, includ- ing haar, db6, sym6, bior2.2 and bior3.3. Original field spectrum is processed by 3 stage liftings based on bior4.4 to denoise the trivial noise-contaminated regions. On processing the water band signal, logarithm transform is firstly taken. And then the spectrum is denoised via LWT based on bior4.4. The results show that an excellent denoised spectrum can be get, especially between 350 nm and 1 800 nm, and between 1 960 nm to 2 500 nm. While there is still a bump around 1 900 nm, this maybe due to the spectrum machine＇s limited precision.
文摘In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuses on deep learning-based approaches to improve the quality of watermarked images,but they have some flaws.To overcome this,the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital image.Recently,quantum logistic maps,which combine the concept of quantum computing with traditional techniques,have been considered a niche and promising area of research that has attracted researchers’attention to further research in digital watermarking.This research uses the chaotic behaviour of the quantum logistic map with Rivest–Shamir–Adleman(RSA)and Secure Hash(SHA-3)algorithms for a robust watermark embedding process,where a watermark is embedded into the host image.This way,the quantum chaos method not only helps limit the chance of tampering with the image content through reverse engineering but also assists in maintaining a high level of imperceptibility and strong robustness with efficient extraction or detection of watermark images.Lifting Wavelet Transformation(LWT)is a potential and computationally efficient version of traditional Discrete Wavelet Transform(DWT)where the host image is divided into four sub-bands to offer a multi-resolution view of an image with greater flexibility in watermarking methodologies.Furthermore,considering the robustness against attacks,a pre-trained Residual Neural Network(ResNet-50),a convolutional neural network with 50 layers deep,is used to better learn the complex features and efficiently extract the watermark from the image.By integrating RSA and SHA-3 algorithms,the proposed model demonstrates improved imperceptibility,robustness,and accuracy in watermark extraction compared to traditional methods.It achieves a Peak Signal-to-Noise Ratio(PSNR)of 49.83%,a Structural Similarity Index Measure(SSIM)of 0.98,and a Number of Pixels Change Rate(NPCR)of 99.79%,respectively.These results reflect the model’s effectiveness in delivering superior quality and security.Consequently,our proposed approach offers accurate results,exceptional invisibility,and enhanced robustness compared to the existing digital image watermarking techniques.
文摘We present a novel quantization-based digital audio walermarking scheme inwavelet domain. By quantizing a host audio's wavelet coefficients (Integer Lifting WaveletTransform) and utilizing the characteristics of human auditory system (HAS), the grayimage isembedded using our watermarking method. Experimental results show that the proposed watermarkingscheme is inaudible and robust against various signal processing such as noising adding, lossycompression, low pass filtering, re-sampling, and re-quantifying.
基金supported by National Natural Science Foundation of China(No.51177142)Natural Science Foundation of Hebei Province(No.F2012203063)
文摘Technological advancement of measurement systems has enhanced the accuracy of power quality assessment by using a combination of measured information. This paper proposes a novel approach for estimating power quality based on information fusion technique of Dempster-Shafer(D-S) evidence theory. First, in order to accurately extract transient features regarding power quality indexes, wavelet packet transform and lifting wavelet transform are proposed to detect various disturbance signals measurement. By using many kinds of transformed transient indexes and steady state indexes, a novel reliability distribution function is constructed,and synthesized assessment index of power quality is drafted based on information fusion technique of D-S evidence theory. Finally,the simulation results prove that D-S evidence theory is a more effective means for evaluating the power quality.
基金supported in part by the National Natural Science Foundation of China (No.61972103)the Natural Science Foundation of Guangdong Province of China (No.2019A1515011361)+2 种基金the Postgraduate Education Innovation Project of Guangdong Ocean University of China (No.202143)the Guangdong Postgraduate Education Innovation Project of China (No.2020JGXM059)the Key Scientific Research Project of Education Department of Guangdong Province of China (2020ZDZX3064).
文摘Traditional image encryption algorithms transform a plain image into a noise-like image.To lower the chances for the encrypted image being detected by the attacker during the image transmission,a visually meaningful image encryption scheme is suggested to hide the encrypted image using another carrier image.This paper proposes a visually meaningful encrypted image algorithm that hides a secret image and a digital signature which provides authenticity and confidentiality.The recovered digital signature is used for the purpose of identity authentication while the secret image is encrypted to protect its confidentiality.Least Significant Bit(LSB)method to embed signature on the encrypted image and Lifting Wavelet Transform(LWT)to generate a visually meaningful encrypted image are designed.The proposed algorithm has a keyspace of 139.5-bit,a Normalized Correlation(NC)value of 0.9998 which is closer to 1 and a Peak Signal to Noise Ratio(PSNR)with a value greater than 50 dB.Different analyses are also performed on the proposed algorithm using different images.The experimental results show that the proposed scheme is with high key sensitivity and strong robustness against pepper and salt attack and cropping attack.Moreover,the histogram analysis shows that the original carrier image and the final visual image are very similar.
基金funded by Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R235)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the web.Usually, the copyright information is embossed over the image in the form ofa logo at the corner or diagonal text in the background. However, this formof visible watermarking is not suitable for a large class of applications. In allsuch cases, a hidden watermark is embedded inside the original image as proofof ownership. A large number of techniques and algorithms are proposedby researchers for invisible watermarking. In this paper, we focus on issuesthat are critical for security aspects in the most common domains like digitalphotography copyrighting, online image stores, etc. The requirements of thisclass of application include robustness (resistance to attack), blindness (directextraction without original image), high embedding capacity, high Peak Signalto Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM). Mostof these requirements are conflicting, which means that an attempt to maximizeone requirement harms the other. In this paper, a blind type of imagewatermarking scheme is proposed using Lifting Wavelet Transform (LWT)as the baseline. Using this technique, custom binary watermarks in the formof a binary string can be embedded. Hu’s Invariant moments’ coefficientsare used as a key to extract the watermark. A Stochastic variant of theFirefly algorithm (FA) is used for the optimization of the technique. Undera prespecified size of embedding data, high PSNR and SSIM are obtainedusing the Stochastic Gradient variant of the Firefly technique. The simulationis done using Matrix Laboratory (MATLAB) tool and it is shown that theproposed technique outperforms the benchmark techniques of watermarkingconsidering PSNR and SSIM as quality metrics.
基金supported by the National Science,Technology and Innovation Plan(NSTIP)Strategic Technologies Programs of the Kingdom of Saudi Arabia under Grant No.08-INF325-02
文摘In this paper, we present a new technique for mammogram enhancement using fast dyadic wavelet transform （FDyWT） based on lifted spline dyadic wavelets and normalized Tsallis entropy. First, a mammogram image is decom- posed into a multiscale hierarchy of low-subband and high-subband images using FDyWT. Then noise is suppressed using normalized Tsallis entropy of the local variance of the modulus of oriented high-subband images. After that, the wavelet coefficients of high-subbands are modified using a non-linear operator and finally the low-subband image at the first scale is modified with power law transformation to suppress background. Though FDyWT is shift-invariant and has better poten- tial for detecting singularities like edges, its performance depends on the choice of dyadic wavclcts. On the other hand, the nulnber of vanishing moments is an important characteristic of dyadic wavelets for singularity analysis because it provides an upper bound measurement for singularity characterization. Using lifting dyadic schemes, we construct lifted spline dyadic wavelets of different degrees with increased number of vanishing moments. We also examine the effect of these wavelets on mammogram enhancement. The method is tested on mammogram images, taken from MIAS （Mammographic Image Analysis Society） database, having various background tissue types and containing different abnormalities. The comparison with tile state-of-the-art contrast enhancement methods reveals that the proposed method performs better and the difference is statistically significant.
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.
