Research On Image Restoration Technology Based On Sparse Representation Model

With the rapid development of information technology, mobile internet, solocial network, and the boom of Big Data, digitized media technology has been found everywhere and been widely applied in various fields of human society. Image and Video are becoming the main carriers of visual signals for digital multimedia content. The quality of digtal images plays a significant role in the process of visual perception and communication. The degradations of digital im-ages are inevitablly caused by many factors during the period of image acquisi-tion, storage, transmission and processing. Therefore, image restoration technol-ogy, which infers the recovery of the original image from the observed degraded version, has been a hot and basic topic in the field of image processing. The re-search of image restoration has a theoretical and practical significance. Nowa-days, image restoration has evolved into an energetic field at the intersection of image processing, computer vision, and computational imaging. Due to the in-formation loss in the process of image degradation, image restoration as an in-verse linear problem is usually ill-posed. The prior models of natural images have important impact for solving the problems of image restoration. The utiliza-tion of natural image prior models is able to constrain the solution space, and enable the inverse problem well-posed, achieving the restored images that are coincident with the characteristics of human visual perception. In this thesis, based on the sparse representation prior modeling of natural images, we mainly focus on the research topics on five image restoration problems: image inpainting, image deblurring, image noise removal, image super-resolution, and image com-pressive sensing recovery. The contents of the thesis can be divided into four sections that are detailed as follows.First, a novel image restoration algorithm based on a joint sparse statistical modeling in space-transform domain is proposed. Traditional image prior models regularized image restoration algorithms usually have two shortcomings. On one hand, only one image property used in regularization-based framework is not enough to obtain satisfying restoration results. On the other hand, the image property of nonlocal self-similarity should be characterized by a more powerful manner, rather than by the traditional weighted graph. To rectify the above prob-lems, from the perspective of image statistics, we first establish two sparse statis-tical models by characterizing image local smoothness in two-dimensional space domain at pixel level, and image nonlocal self-similarity in three-dimensional transform domain at patch level, respectively, and then merge the two models into a novel joint sparse statistical modeling in hybrid space-transform domain, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Under the regularization-based framework, a new form of minimization func-tional for solving image inverse problem using the joint sparse statistical model-ing is formulated, which is accompanied by an effective choice for the corre-sponding regularization parameters. Extensive experiments on image inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise removal appli-cations verify the effectiveness of the proposed algorithm.Second, a novel image restoration algorithm based on structural group sparse representation (SGSR) is proposed. Traditional patch-based sparse repre-sentation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high computational com-plexity in dictionary learning. Second, each patch is considered independently in dictionary learning and sparse coding, which ignores the relationship among patches, resulting in inaccurate sparse coding coefficients. To rectify the above problems, instead of using patch as the basic unit of sparse representation, this thesis exploits the concept of structural group as the basic unit of sparse repre-sentation, which is composed of nonlocal patches with similar structures, and es-tablishs a novel sparse representation modeling of natural images, called struc-tural group sparse representation (SGSR). The proposed SGSR is able to sparsely represent natural images in the domain of structural group, which enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. Under the regularization-based framework, a new form of minimization functional for solving image inverse problem via SGSR in the form of L0norm is formulated, associated with an effective self-adaptive dictionary learning method for each structural group with low complexity. Extensive ex- periments on image inpainting, image deblurring and image compressive sensing recovery manifest that the proposed SGSR modeling outperforms many current state-of-the-art schemes in both PSNR and visual perception.Third, a new algorithm for image compressive sensing recovery using adap-tively learned sparsifying basis via L0minimization is proposed. Most of the conventional CS recovery approaches exploited a set of fixed bases (e.g. DCT, wavelet and gradient domain) for the entirety of a signal, which are irrespective of the non-stationarity of natural signals and cannot achieve high enough degree of sparsity, thus resulting in poor CS recovery performance and restricting the CS application in natural images. This thesis introduces the patch-based redundant sparse representation model that is used to characterize the intrinsic sparsity of the whole image into the problem of image compressive sensing recovery, and develops a framework for image compressive sensing recovery using the adap-tively learned sparsifying basis in the form of L0norm. The role of the adaptive learned sparsifying basis in the form of L0is to achieve high enough degree of adaptability and sparsity, thus greatly confining the CS solution. The role of the patch-based redundant sparse representation model is to reduce blocking artifacts and obtain the CS results with high visual quality. Experimental results on a wide range of natural images for CS recovery have shown that the proposed algorithm achieves significant performance improvements over many current state-of-the-art schemes.Fourth, a novel image super-resolution algorithm via dual-dictionary learn-ing and sparse representation is proposed. Aiming at reducing the large gap of the frequency in the low and high resolution images exhibited in the traditional dictionary learning and sparse representation based image super-resolution methods and recovering more image high-frequency information, in this thesis, the high-frequency (HF) to be estimated in super-solution is considered as a combination of two components: main high-frequency (MHF) and residual high-frequency (RHF). As for above two types of high-frequency, two corre-sponding dictionaries are successively learned by making use of training images via sparse representation, i.e., the main dictionary and the residual dictionary. In the process of image super-resolution, the input low resolution image is first magnified and added the reconstructed main high-frequency information by vir- tue of the main dictionary, thus generating a temporary image. Then, by pre-forming the same reconstruction scheme with the temporary image and the re-sidual dictionary, the reconstructed residual high-frequency information is recon-structed, leading to the final high-resolution image. Extensive experimental re-sults on test images validate that by employing the proposed two-layer progres-sive scheme, more image details can be recovered and much better results can be achieved.