Adaptive Curvature-Guided Image Filtering for Structure + Texture Image Decomposition

Belyaev, Alexander; Fayolle, Pierre-Alain

doi:10.1109/tip.2018.2852741

Cited by 22 publications

(17 citation statements)

References 57 publications

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“…Belyaev et al . [27] is based on the TV model and considers the curvature change on the level set in the weight of the regularization term to maintain the weak edge structure. The TV model was first proposed by Rudin et al .…”

Section: Related Workmentioning

confidence: 99%

Low‐light image enhancement based on exponential Retinex variational model

Chen

Hua

2021

IET Image Processing

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Aiming at the problems of residual noise, low contrast, and limited detail information caused by low-light images, this paper proposes a new Retinex variational model. According to Retinex theory, it is necessary to estimate the illumination and reflectance components decomposed from the original image. In order to better maintain the edge information, texture richness, and prevent artefacts, the exponential forms of local variation deviation and total variation are used as illumination prior and reflectance prior, respectively, and mixed norms are used to constrain them, so as to deal with the illumination information and texture details of the image more effectively, and then use the bright channel prior to improve the colour reproduction sense of the original image, thereby constructing the objective function, and finally using the alternating iterative optimization method to find the optimal solution to the proposed model. Experiments show that compared with other existing image enhancement methods, the method proposed here improves the contrast of the image, overcomes the phenomenon of halo artefacts and colour distortion, is more consistent with human vision, and produces better results in terms of quantitative performance.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

Low‐light image enhancement based on exponential Retinex variational model

Chen

Hua

2021

IET Image Processing

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“…The problem of separating textures from the underlying structure is a well-studied one; see e.g. [44], [43], [45], [8], [46], [42], [39], [47], [40]. While the classical bilateral filter can remove fine textures from an image, it cannot directly be used for removing coarse textures.…”

Section: Texture Filteringmentioning

confidence: 99%

Fast Adaptive Bilateral Filtering

Gavaskar

Chaudhury

2019

IEEE Trans. on Image Process.

123

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In the classical bilateral filter, a fixed Gaussian range kernel is used along with a spatial kernel for edge-preserving smoothing. We consider a generalization of this filter, the socalled adaptive bilateral filter, where the center and width of the Gaussian range kernel is allowed to change from pixel to pixel. Though this variant was originally proposed for sharpening and noise removal, it can also be used for other applications such as artifact removal and texture filtering. Similar to the bilateral filter, the brute-force implementation of its adaptive counterpart requires intense computations. While several fast algorithms have been proposed in the literature for bilateral filtering, most of them work only with a fixed range kernel. In this paper, we propose a fast algorithm for adaptive bilateral filtering, whose complexity does not scale with the spatial filter width. This is based on the observation that the concerned filtering can be performed purely in range space using an appropriately defined local histogram. We show that by replacing the histogram with a polynomial and the finite range-space sum with an integral, we can approximate the filter using analytic functions. In particular, an efficient algorithm is derived using the following innovations: the polynomial is fitted by matching its moments to those of the target histogram (this is done using fast convolutions), and the analytic functions are recursively computed using integration-by-parts. Our algorithm can accelerate the brute-force implementation by at least 20×, without perceptible distortions in the visual quality. We demonstrate the effectiveness of our algorithm for sharpening, JPEG deblocking, and texture filtering.

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“…Image decomposition is one of the major goals of image processing. Recently, image decomposition has occured in the areas of optical coherence tomography images and multi-focus image fusion [1][2][3][4][5]. For a given original image f, our goal is to split f into the structural component u containing the geometrical information and the textural component v containing the textural information [6].…”

Section: Introductionmentioning

confidence: 99%

Structure–texture image decomposition using a new non‐local TV‐Hilbert model

2020

IET image process

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Combining the advantages of the non‐local total variation (TV) and the Gabor function, a new Gabor function based non‐local TV‐Hilbert model is presented to separate the structure and texture components of the image. Computationally, by introducing the dual form of the non‐local TV, the authors reformulate the non‐local TV‐Hilbert minimisation problem into a convex–concave saddle‐point problem. In the aspect of solving algorithm, by transforming the Chambolle–Pock's first‐order primal–dual algorithm into a different equivalent form. The authors propose a proximal‐based primal–dual algorithm to solve the convex–concave saddle‐point problem. At last, experimental results demonstrate that the proposed new model outperforms several existing state‐of‐the‐art variational models.

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Adaptive Curvature-Guided Image Filtering for Structure + Texture Image Decomposition

Cited by 22 publications

References 57 publications

Low‐light image enhancement based on exponential Retinex variational model

Low‐light image enhancement based on exponential Retinex variational model

Fast Adaptive Bilateral Filtering

Structure–texture image decomposition using a new non‐local TV‐Hilbert model

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