Cartoon-Texture Image Decomposition using Orientation Characteristics in Patch Recurrence

Xu, Ruotao; Xu, Youming; Quan, Yuhui; Gao, Hui

doi:10.1137/19m128898x

Cited by 9 publications

(3 citation statements)

References 53 publications

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“…1 explicitly or implicitly. The patch-based models [29], [30], [52], for example, characterize a low-rank nature of image structures or textures over non-local image patches. It is possible for them to produce better decomposition results but the performance is at the expense of searching local similar patches.…”

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%

Semi-Sparsity for Smoothing Filters

Huang

Wang

et al. 2023

IEEE Trans. on Image Process.

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“…Many models use the space of oscillatory functions equipped with appropriate norms able to represent textured or oscillatory patterns [29,30,33]. An alternative approach assumes that, under suitable conditions, textures can be sparsified, i.e., a texture patch can be represented by few atoms in a given dictionary or by specific transforms [34].…”

Section: Cartoon-texture Decompositionmentioning

confidence: 99%

Cartoon-texture evolution for two-region image segmentation

Antonelli¹,

Simone²,

Viola³

2022

Preprint

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Two-region image segmentation is the process of dividing an image into two regions of interest, i.e., the foreground and the background. To this aim, Chan et al.[1] designed a model well suited for smooth images. One drawback of this model is that it may produce a bad segmentation when the image contains oscillatory components. Based on a cartoon-texture decomposition of the image to be segmented, we propose a new model that is able to produce an accurate segmentation of images also containing noise or oscillatory information like texture. The novel model leads to a non-smooth constrained optimization problem which we solve by means of the ADMM method. The convergence of the numerical scheme is also proved. Several experiments on smooth, noisy, and textural images show the effectiveness of the proposed model.

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“…Automated recognition of these printed characters or writing enhances the level of automation in inspection processes and improves authentication accuracy. However, owing to the surface complexity of textured materials, printing conditions, the similarity of adjacent regions or patterns (e.g., guilloche patterns or watermarks), and variations in illumination, the segmentation of printed characters or writing remains a persistent challenge, potentially compromising the accuracy of character recognition [2], [3], [4]. Consequently, effective denoising becomes paramount before undertaking character segmentation and recognition.…”

Section: Introductionmentioning

confidence: 99%

Denoising on Textured Image Using Total Generalized Variation With Overlapping Group Sparsity Based on Fast Split Bregman Method

Zhang,

Yen

2024

IEEE Access

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The Total Generalized Variation (TGV) model proves effective in removing texture and background patterns in natural images while suppressing the staircase effect introduced by traditional Total Variation (TV) regularization. Nevertheless, TGV falls short in preserving structural features due to its lack of consideration for such structural features. This paper introduces Overlapping Group Sparsity (OGS) regularization into the TGV model with the specific aim of enhancing denoising, particularly in textured images. By leveraging prior knowledge of sparse structures discernible from first-order and second-order gradients, this model surpasses the conventional TGV models in achieving superior denoising and staircase effect elimination. The model proposed employs a fast split Bregman iteration method to address the L1 regularization problem within the complex TGV model combined with OGS. The experimental results comparing various state-of-the-art denoising TV-and TGV-based models highlight a significant improvement in the denoising performance of the proposed model. Specifically, in comparison to the average values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) obtained from other state-of-the-art models, the proposed model demonstrated improvements of 3.5% in PSNR and 3.4% in SSIM.INDEX TERMS Total generalized variation, overlapping group sparsity, texture denoising, split Bregman.

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Cartoon-Texture Image Decomposition using Orientation Characteristics in Patch Recurrence

Cited by 9 publications

References 53 publications

Semi-Sparsity for Smoothing Filters

Semi-Sparsity for Smoothing Filters

Cartoon-texture evolution for two-region image segmentation

Denoising on Textured Image Using Total Generalized Variation With Overlapping Group Sparsity Based on Fast Split Bregman Method

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