Structure–texture image decomposition via non-convex total generalized variation and convolutional sparse coding

Wang, Chunxue; Xu, Linlin; Liu, Ligang

doi:10.1007/s00371-021-02392-z

Cited by 8 publications

(1 citation statement)

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“…It is possible for them to produce better decomposition results but the performance is at the expense of searching local similar patches. Besides, the frequency filters, transformed domain analysis [53], and sparse representation techniques [54] are also proposed for structural and textural analysis, while the performance is generally limited in some typical cases and not universally applicable in many practical applications. In addition, it has also been witnessed that some existing methods [55] strive to decompose an image into more than two basic components -for example, structures, textures, and random noise.…”

Section: Extensive Analysismentioning

confidence: 99%

Semi-Sparsity for Smoothing Filters

Huang

Wang

et al. 2023

IEEE Trans. on Image Process.

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In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimization scheme. The model is derived from the observations that semi-sparsity prior knowledge is universally applicable in situations where sparsity is not fully admitted such as in the polynomial-smoothing surfaces. We illustrate that such priors can be identified into a generalized L0-norm minimization problem in higher-order gradient domains, giving rise to a new "feature-aware" filter with a powerful simultaneous-fitting ability in both sparse singularities (corners and salient edges) and polynomial-smoothing surfaces. Notice that a direct solver to the proposed model is not available due to the non-convexity and combinatorial nature of L0-norm minimization. Instead, we propose to solve it approximately based on an efficient half-quadratic splitting technique. We demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications.

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Section: Extensive Analysismentioning

confidence: 99%