IEEE Trans Image Process - Group-based sparse representation for image restoration.

Tópicos

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Resumo

Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high computational complexity 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. In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR). The proposed GSR is able to sparsely represent natural images in the domain of group, which enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. In addition, an effective self-adaptive dictionary learning method for each group with low complexity is designed, rather than dictionary learning from natural images. To make GSR tractable and robust, a split Bregman-based technique is developed to solve the proposed GSR-driven l0 minimization problem for image restoration efficiently. Extensive experiments on image inpainting, image deblurring and image compressive sensing recovery manifest that the proposed GSR modeling outperforms many current state-of-the-art schemes in both peak signal-to-noise ratio and visual perception.

Resumo Limpo

tradit patchbas spars represent model natur imag usual suffer two problem first solv largescal optim problem high comput complex dictionari learn second patch consid independ dictionari learn spars code ignor relationship among patch result inaccur spars code coeffici paper instead use patch basic unit spars represent exploit concept group basic unit spars represent compos nonloc patch similar structur establish novel spars represent model natur imag call groupbas spars represent gsr propos gsr abl spars repres natur imag domain group enforc intrins local sparsiti nonloc selfsimilar imag simultan unifi framework addit effect selfadapt dictionari learn method group low complex design rather dictionari learn natur imag make gsr tractabl robust split bregmanbas techniqu develop solv propos gsrdriven l minim problem imag restor effici extens experi imag inpaint imag deblur imag compress sens recoveri manifest propos gsr model outperform mani current stateoftheart scheme peak signaltonois ratio visual percept

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