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

Rongfang Wang,Huan Zheng,Yali Qin,Zhenbiao Wang

doi:10.3390/electronics11020182

Rongfang Wang, Huan Zheng + Show 2 more

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https://doi.org/10.3390/electronics11020182

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Journal: Electronics	Publication Date: Jan 7, 2022
Citations: 4	License type: CC BY 4.0

Affiliation: Zhejiang University of Technology

Abstract

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.

Highlights

Compressed sensing (CS) [1,2,3] is a signal processing technique that allows for successful signal reconstruction with fewer measurements than Nyquist sampling [4]
To validate the performance of the proposed model group-based sparse representation image restoration model (GSR)-JR, the proposed GSR-JR model is compared with five existing image reconstruction models, total variation (TV)-NLR, MH-based compressed sensing (BCS)-SPL, adaptive sparse non-local regularization CS reconstruction model (ASNR), GSR, and GSRC-NLR
When the sensing rate is 0.1, the average peak signal-to-noise ratio (PSNR) (SSIM) of the GSR-JR model is improved by 3.78 dB (0.0929), 3.72 dB (0.1291), 1.40 dB (0.0227), 1.80 dB (0.0298), and 1.164 dB (0.0209), respectively, compared with the other models

Summary

Introduction

Compressed sensing (CS) [1,2,3] is a signal processing technique that allows for successful signal reconstruction with fewer measurements than Nyquist sampling [4]. In 2013, Zhang et al [16] introduced non-local similarity [17] as a regularization constraint into the TV model and proposed a non-local [18] regularization total variation model (TV-NLR) This model preserves the edges and details of the image and promotes the development of TV-based image CS reconstruction. The model employs the principal component analysis (PCA) [22] algorithm to learn dictionaries from the preliminary reconstruction of the image rather than genuine images, which reduces computational complexity and adds non-local similarity to preserve the image’s edges and details. It promotes the further development of the patch-based sparse representation image CS reconstruction model

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