A Hybrid Sparse Representation Model for Image Restoration.

Caiyue Zhou,Lin Sun,Chuanyong Zhang,Dongmei Wu,Yanfen Kong,Chongbo Zhou

doi:10.3390/s22020537

Caiyue Zhou, Lin Sun + Show 4 more

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https://doi.org/10.3390/s22020537

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Abstract

Group-based sparse representation (GSR) uses image nonlocal self-similarity (NSS) prior to grouping similar image patches, and then performs sparse representation. However, the traditional GSR model restores the image by training degraded images, which leads to the inevitable over-fitting of the data in the training model, resulting in poor image restoration results. In this paper, we propose a new hybrid sparse representation model (HSR) for image restoration. The proposed HSR model is improved in two aspects. On the one hand, the proposed HSR model exploits the NSS priors of both degraded images and external image datasets, making the model complementary in feature space and the plane. On the other hand, we introduce a joint sparse representation model to make better use of local sparsity and NSS characteristics of the images. This joint model integrates the patch-based sparse representation (PSR) model and GSR model, while retaining the advantages of the GSR model and the PSR model, so that the sparse representation model is unified. Extensive experimental results show that the proposed hybrid model outperforms several existing image recovery algorithms in both objective and subjective evaluations.

Highlights

The purpose of image restoration is to reconstruct high-quality images x from the degraded images y
The contributions of this paper are summarized as follows: (1) We propose a hybrid sparse representation model that combines the nonlocal self-similarity (NSS) priori of degraded images and external image dataset to make full use of the specific structure of degraded image and the common characteristics of natural image; (2) The introduction of joint model into the HSR retains the advantages of the patch-based sparse representation (PSR) model and Group-based sparse representation (GSR) model, and alleviates their respective disadvantages
The peak signal to noise ratio (PSNR) and structural similarity (SSIM) [44] metrics were used for the experimental comparison of the restored images

Summary

Introduction

The purpose of image restoration is to reconstruct high-quality images x from the degraded images y. This is a typical inverse problem, and its mathematical expression is y = Hx + n (1). Where H denotes the degenerate operator and n is usually assumed to be zero-mean Gaussian white noise. Equation (1) can represent different image processing tasks. We focus on the image restoration task. In order to obtain high-quality reconstructed images, image prior knowledge is usually used to regularize the solution space. Image restoration can be expressed as the following minimization problems: x

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Results

Conclusion