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Lossless digital image watermarking in sparse domain by using k-singular value decomposition algorithm

IET IMAGE PROCESSING 2020年第6期14卷 1005-1014页

作者： Deeba, Farah kun, She Dharejo, Fayaz Ali Zhou, Yuanchun Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Sichuan Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China

The crucial hurdle faced by the watermarking technique is to maintain the steadiness corresponding to several attacks while assisting a sufficient level of security. In this study, a robust lossless sparse domain-based watermarking approach combined with discrete cosine transform (DCT) is introduced to hide the secret message in the selected significant sparse elements of the host image. The proposed method takes advantage of a sparse representation-based dictionary learning process. To enhance the security of the original image, the authors first apply the DCT on a secret message. These DCT coefficients with some regularised parameters will be inserted into the selected significant sparse coefficients. At the extraction stage, the secret message is extracted from those significant sparse coefficients by employing the sparse domain orthogonal matching pursuit algorithm. Finally, the inverse DCT is applied to extract the secret message without any information loss. To show the effectiveness of the proposed method, different commonly used attacks are simulated. Simulation results in terms of peak signal-to-noise ratio, structural similarity, normal correlation, and feature similarity indicate that the proposed method can recover the hidden secret message accurately against seven different types of attacks including speckle, Gaussian, salt and pepper, rotate, crop, fold, and blur attack.

关键词： discrete cosine transforms iterative methods singular value decomposition image watermarking data encapsulation image representation image coding lossless digital image watermarking k-singular value decomposition algorithm watermarking technique sparse elements host image DCT coefficients sparse coefficients sparse domain orthogonal matching pursuit algorithm inverse DCT hidden secret message robust lossless sparse domain-based watermarking approach discrete cosine transform sparse representation-based dictionary learning process regularised parameters secret message extraction stage peak signal-to-noise ratio structural similarity normal correlation feature similarity Gaussian attack salt and pepper attacks speckle attacks rotate attacks crop attacks fold attacks blur attack

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Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm

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IET IMAGE PROCESSING 2020年第11期14卷 2365-2375页

作者： Deeba, Farah kun, She Ali Dharejo, Fayaz Zhou, Yuanchun Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China

It is very interesting to reconstruct high-resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super-resolution method for CT medical images in the sparse representation domain with dictionary learning. The sparse coupled k-singular value decomposition (kSVD) algorithm is employed for dictionary learning purposes. Images are divided into two sets of low resolution (LR) and high resolution (HR), to improve the quality of low-resolution images, the authors prepare dictionaries over LR and HR image patches using the kSVD algorithm. The main idea behind the proposed method is that sparse coupled dictionaries learn about each patch and establish the relationship between sparse coefficients of LR and HR image patches to recover the HR image patch for LR image. The proposed method is compared to conventional algorithms in terms of mean peak signal-to-noise ratio and structural similarity index measurements by using three different data set images, including CT chest, CT dental and CT brain images. The authors also analysed the proposed improved method for different dictionary sizes and patch size to obtain a similar high-resolution image. These parameters play an essential role in the reconstruction of the HR images.

关键词： medical image processing image representation diseases image reconstruction singular value decomposition dictionaries image resolution brain image denoising computerised tomography learning (artificial intelligence) sparse coupled dictionaries sparse coefficients image patches HR image patch LR image conventional algorithms different dictionary sizes high-resolution image HR images computed tomography images reconstruction coupled dictionary high-resolution reconstruction-based methods high-resolution images image processing applications image analysis medical imaging high-resolution computed tomography medical images super-resolution method CT medical images sparse representation domain k-singular value decomposition algorithm dictionary learning purposes low-resolution images kSVD algorithm

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3D inverse synthetic aperture radar image quality improvement using sparse signal representation

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IET RADAR SONAR AND NAVIGATION 2023年第3期17卷 388-407页

作者： Mehrpooya, Ali karbasi, Seyed Mohammad Nazari, Milad Abbasi, Zahra Nayebi, Mohammad Mahdi Sharif Univ Technol Dept Elect Engn Tehran Iran Malek Ashtar Univ Technol Dept Elect Engn Tehran Iran

Generalisation of one-dimensional dictionary learning (1DDL) algorithms to Multidimensional (MD) mode and its utilisation in MD data applications, increases the speed and reduces the computational complexity. An example of such an application is 3D inverse synthetic aperture radar (ISAR) image reconstruction and noise reduction. In this study, in addition to MD mode generalisation, the formulation structure of the multidimensional dictionary learning (MDDL) problem is discussed followed by two novel algorithms to solve it. The first one is based on the k-singular value decomposition algorithm for 1DDL, which uses alternating minimisation and singular value decomposition. The second algorithm is the extension of the sequential generalisation of k-means 1DDL algorithm to the MD mode. Moreover, the MD tensor denoising method based on the MDDL algorithm (MDDL-ALG) is proposed. As an application, the proposed method is used to denoise the 3D ISAR image. The numerical simulations reveal that the proposed methods, in addition to reducing the memory consumption and the computational complexity, also enjoy higher convergence rate in comparison to 1D algorithms. Specifically, convergence speed of MD algorithms, depending on the training data size, is up to at least 10 times faster than the equivalent 1D counterparts. As revealed through the simulations, the amount of signal to noise ratio recovered by the proposed methods is almost 2 dB higher than the case using a pre-designed dictionary for denoising. Moreover, it outperforms about 10 dB over the case with the conventional 3D-IFFT method for image construction.

关键词： noise reduction Optimisation techniques Signal processing and detection 3D inverse synthetic aperture radar image quality improvement Radar equipment, systems and applications MDDL algorithm formulation structure Algebra MDDL-ALG minimisation synthetic aperture radar MD tensor denoising method image construction singular value decomposition multidimensional mode Radar theory radar imaging Optical, image and video signal processing Computer vision and image processing techniques image denoising MD data applications sequential generalisation multidimensional dictionary learning problem MD mode generalisation 1DDL algorithm 3D ISAR image reconstruction convergence speed 3D-IFFT method signal representation 3D inverse synthetic aperture radar image reconstruction Interpolation and function approximation (numerical analysis) MD algorithms one-dimensional dictionary learning algorithms sparse signal representation equivalent 1D counterparts pre-designed dictionary image reconstruction k-singular value decomposition algorithm training data size fast Fourier transforms computational complexity

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Sparse representation-based quasi-clean speech construction for speech quality assessment under complex environments

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IET SIGNAL PROCESSING 2017年第4期11卷 486-493页

作者： Zhou, Weili He, Qianhua Wang, Yalou Li, Yanxiong South China Univ Technol Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China

A non-intrusive speech quality assessment method for complex environments was proposed. In the proposed approach, a new sparse representation-based speech reconstruction algorithm was presented to acquire the quasi-clean speech from the noisy degraded signal. Firstly, an over-complete dictionary of the clean speech power spectrum was learned by the k-singular value decomposition algorithm. Then in the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the noise spectrum which was adjusted by a posteriori SNR-weighted factor, and the orthogonal matching pursuit approach was applied to reconstruct the clean speech spectrum from the noisy speech. The quasi-clean speech was considered as the reference to a modified PESQ perceptual model, and the mean opinion score of the noisy degraded speech was achieved via the distortions estimation between the quasi-clean speech and the degraded speech. Experimental results show that the proposed approach obtains a correlation coefficient of 0.925 on NOIZEUS complex environment database, which is 99% similar to the performance of the intrusive standard ITU-T PESQ, and 7.1% outperforms non-intrusive standard ITU-T P.563.

关键词： speech processing signal representation signal reconstruction signal denoising singular value decomposition correlation methods iterative methods sparse representation-based quasiclean speech construction complex environments nonintrusive speech quality assessment method sparse representation-based speech reconstruction algorithm noisy degraded signal over-complete dictionary clean speech power spectrum k-singular value decomposition algorithm stopping residue error cross-correlation noise spectrum a posteriori SNR-weighted factor orthogonal matching pursuit modified PESQ perceptual model mean opinion score correlation coefficient NOIZEUS complex environment database

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Separation-based model for low-dose CT image denoising

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JOURNAL OF ENGINEERING-JOE 2020年第12期2020卷 1198-1208页

作者： Chen, Wenbin Bai, Junjie Gu, Xiaohua Li, Yuyan Shao, Yanling Zhang, Quan Liu, Yi Liu, Yanli Gui, Zhiguo Chongqing Univ Sci & Technol Sch Elect Engn Chongqing 401331 Peoples R China Fujian Univ Technol Fujian Prov Univ Engn Res Ctr Ind Automat Fuzhou 350108 Fujian Peoples R China North Univ China Sch Sci Taiyuan 030051 Peoples R China North Univ China Shanxi Prov Key Lab Biomed Imaging & Big Data Taiyuan 030051 Peoples R China

Low-dose computed tomography (LDCT) image often contains mottle noise and streak artefacts, which seriously interfere with clinical diagnosis. In this study, the separation-based (SEPB) method is proposed for mottle noise and streak artefacts suppression and structure preservation. In it, the LDCT image is decomposed into the structural image with residual mottle noise and the streak artefacts image with residual structural details by the image decomposition structural-preserving image smoothing method. The structural image is filtered by the k-singular value decomposition algorithm to remove the residual mottle noise, and the structural details in the streak artefacts image are extracted by the morphological component analysis theory. The extracted structural details are added to the filtered structural image to get the LDCT result image. Meanwhile, in the process of extracting the structural details, the streak artefacts dictionary learned from the streak artefacts image is corrected by the local intuitional fuzzy entropy to remove its structural atoms. The experiments are conducted on the modified Shepp-Logan phantom, the pelvis phantom and the clinical abdominal data to evaluate the proposed SEPB method. Compared to several comparative denoising methods, the experimental results show that the SEPB method has better performance in subjective visual effect and objective indicators.

关键词： singular value decomposition image denoising phantoms smoothing methods computerised tomography medical image processing filtered structural image streak artefacts image structural atoms low-dose CT image low-dose computed tomography image separation-based method residual mottle noise image smoothing method image decomposition structural-preserving image smoothing method local intuitional fuzzy entropy modified Shepp-Logan phantom k-singular value decomposition algorithm

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