scholarly journals Group-Based Sparse Representation for Compressed Sensing Image Reconstruction with Joint Regularization

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 182
Author(s):  
Rongfang Wang ◽  
Yali Qin ◽  
Zhenbiao Wang ◽  
Huan Zheng
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Structural Similarity ◽  
Block Matching ◽  
Reconstructed Image ◽  
Local Similarity ◽  
Iterative Thresholding ◽  
Regularization Term

Achieving high-quality reconstructions of images is the focus of research in image compressed sensing. Group sparse representation improves the quality of reconstructed images by exploiting the non-local similarity of images; however, block-matching and dictionary learning in the image group construction process leads to a long reconstruction time and artifacts in the reconstructed images. To solve the above problems, a joint regularized image reconstruction model based on group sparse representation (GSR-JR) is proposed. A group sparse coefficients regularization term ensures the sparsity of the group coefficients and reduces the complexity of the model. The group sparse residual regularization term introduces the prior information of the image to improve the quality of the reconstructed image. The alternating direction multiplier method and iterative thresholding algorithm are applied to solve the optimization problem. Simulation experiments confirm that the optimized GSR-JR model is superior to other advanced image reconstruction models in reconstructed image quality and visual effects. When the sensing rate is 0.1, compared to the group sparse residual constraint with a nonlocal prior (GSRC-NLR) model, the gain of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) is up to 4.86 dB and 0.1189, respectively.

Image Denoising Using Sparse Representation and Principal Component Analysis

2021 ◽  
pp. 2250033
Author(s):  
Maryam Abedini ◽  
Horriyeh Haddad ◽  
Marzieh Faridi Masouleh ◽  
Asadollah Shahbahrami
Keyword(s):  
Principal Component Analysis ◽  
Sparse Representation ◽  
Image Denoising ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Block Matching ◽  
Discrete Wavelet

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

scholarly journals Encryption Based Image Watermarking Algorithm in 2DWT-DCT Domains

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5540
Author(s):  
Nayeem Hasan ◽  
Md Saiful Islam ◽  
Wenyu Chen ◽  
Muhammad Ashad Kabir ◽  
Saad Al-Ahmadi
Keyword(s):  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reconstructed Image ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Watermarking Scheme

This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.

scholarly journals Super-resolution reconstruction for a single image based on self-similarity and compressed sensing

2018 ◽  
Vol 12 (3) ◽  
pp. 234-244
Author(s):  
Qiang Yang ◽  
Huajun Wang
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Single Image ◽  
Low Resolution ◽  
Self Similarity ◽  
Resolution Image ◽  
Reconstruction Performance

Super-resolution image reconstruction can achieve favorable feature extraction and image analysis. This study first investigated the image’s self-similarity and constructed high-resolution and low-resolution learning dictionaries; then, based on sparse representation and reconstruction algorithm in compressed sensing theory, super-resolution reconstruction (SRSR) of a single image was realized. The proposed algorithm adopted improved K-SVD algorithm for sample training and learning dictionary construction; additionally, the matching pursuit algorithm was improved for achieving single-image SRSR based on image’s self-similarity and compressed sensing. The experimental results reveal that the proposed reconstruction algorithm shows better visual effect and image quality than the degraded low-resolution image; moreover, compared with the reconstructed images using bilinear interpolation and sparse-representation-based algorithms, the reconstructed image using the proposed algorithm has a higher PSNR value and thus exhibits more favorable super-resolution image reconstruction performance.

Reconstruction of Slice Image from Solid Rocket Motor Skiagrams Based on Compressed Sensing Algorithm

2013 ◽  
Vol 321-324 ◽  
pp. 1035-1040
Author(s):  
Zhi Gao Xu ◽  
Chao Ning ◽  
Jing Ma ◽  
Xiang Bin Li
Keyword(s):  
Compressed Sensing ◽  
Rocket Motor ◽  
Reconstructed Image ◽  
Solid Rocket Motor ◽  
Sensing Technology ◽  
Radiographic Interpretation ◽  
Solid Rocket ◽  
Slice Image ◽  
Algebraic Reconstruction

A reconstruction program of slice image based on SolidRocket Motor (SRM) skiagrams is put forward to overcome the deficiency of artificial radiographic interpretation. The algebraic reconstruction algorithmbased on compressed sensing technology is designed. The influence of radiographic interval angle and skiagram sizes on reconstructed slice image is studied. Radiographic interval angle has a great impact on the quality of the reconstructed image. Slender defects are not sensitive to changes in the length of the skiagram, but circular defects are sensitive to changes in the length of the skiagram. The reconstruction tests of model SRM skiagrams show that the sizes and locations of the debonded defects can be easily ascertained and the efficiency of radiographic interpretation can be greatly improved.

Adaptive Dual Conjudate Gradient Projection Algorithm for Compressed Sensing Image Reconstruction

2013 ◽  
Vol 347-350 ◽  
pp. 2600-2604
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Reconstruction Accuracy ◽  
Reconstruction Quality ◽  
Dual Gradient

In order to improve the quality of noise signals reconstruction method, an algorithm of adaptive dual gradient projection for sparse reconstruction of compressed sensing theory is proposed. In ADGPSR algorithm, the pursuit direction is updated in two conjudate directions, the better original signals estimated value is computed by conjudate coefficient. Thus the reconstruction quality is improved. Experiment results show that, compared with the GPSR algorithm, the ADGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

A blind image deblurring algorithm based on relative gradient and sparse representation

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840087 ◽  
Author(s):  
Qiwei Chen ◽  
Yiming Wang
Keyword(s):  
Image Reconstruction ◽  
Image Restoration ◽  
Sparse Representation ◽  
Kernel Estimation ◽  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Overcomplete Dictionary ◽  
Blur Kernel ◽  
Blind Image

A blind image deblurring algorithm based on relative gradient and sparse representation is proposed in this paper. The layered method restores the image by three steps: edge extraction, blur kernel estimation and image reconstruction. The positive and negative gradients in texture part have reversal changes, and the edge part that reflects the image structure has only one gradient change. According to the characteristic, the edge of the image is extracted by using the relative gradient of image, so as to estimate the blur kernel of the image. In the stage of image reconstruction, in order to overcome the problem of oversize of the image and the overcomplete dictionary matrix, the image is divided into small blocks. An overcomplete dictionary is used for sparse representation, and the image is reconstructed by the iterative threshold shrinkage method to improve the quality of image restoration. Experimental results show that the proposed method can effectively improve the quality of image restoration.

A Novel Image Fusion Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 1593-1596
Author(s):  
Shan Shan Liu
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Compressed Sensing ◽  
Image Fusion ◽  
Sparse Representation ◽  
Reconstructed Image ◽  
Fusion Algorithm

Based on the wavelet transform, this study introduced the theory of the compressed sensing algorithm. Then proposed a wavelet transform based compressed sensing algorithm by the better sparse representation ability of the wavelet transform on the image. Finally, this algorithm was compared with the DCT and wavelet transform algorithm. The experiment results show that the reconstructed image quality has a significant improvement. Especially, this algorithm has better effect on the images with rich curve.

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