Fast multi-scale detail decomposition via accelerated iterative shrinkage

Badri, Hicham; Yahia, Hussein; Aboutajdine, Driss

doi:10.1145/2542355.2542397

Cited by 11 publications

(10 citation statements)

References 16 publications

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“…The method proposed by Badri et al [2013; is an exception which is very fast. It shares a similar two-step smoothing procedure with our approach.…”

Section: Global Optimization Based Methodsmentioning

confidence: 99%

Real-time Image Smoothing via Iterative Least Squares

Liu¹,

Zhang²,

Huang³

et al. 2020

Preprint

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Edge-preserving image smoothing is a fundamental procedure for many computer vision and graphic applications. There is a tradeoff between the smoothing quality and the processing speed: the high smoothing quality usually requires a high computational cost which leads to the low processing speed. In this paper, we propose a new global optimization based method, named iterative least squares (ILS), for efficient edge-preserving image smoothing. Our approach can produce high-quality results but at a much lower computational cost. Comprehensive experiments demonstrate that the propose method can produce results with little visible artifacts. Moreover, the computation of ILS can be highly parallel, which can be easily accelerated through either multi-thread computing or the GPU hardware. With the acceleration of a GTX 1080 GPU, it is able to process images of 1080p resolution (1920 × 1080) at the rate of 20fps for color images and 47fps for gray images. In addition, the ILS is flexible and can be modified to handle more applications that require different smoothing properties. Experimental results of several applications show the effectiveness and efficiency of the proposed method. The code is available at https://github.com/wliusjtu/Realtime-Image-Smoothing-via-Iterative-Least-Squares CCS Concepts: • Computing methodologies → Computational photography; Image processing.

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“…The method proposed by Badri et al [2013; is an exception which is very fast. It shares a similar two-step smoothing procedure with our approach.…”

Section: Global Optimization Based Methodsmentioning

confidence: 99%

Real-time Image Smoothing via Iterative Least Squares

Liu¹,

Zhang²,

Huang³

et al. 2020

Preprint

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“…This special alternating optimization strategy is time consuming when the number of iterations needed is large. Please refer to the paper [BYA13] for the accelerated iterative shrinkage algorithm. In our implementation, the accelerated iterative shrinkage algorithm [BYA13] based on first order proximal operators [PB14] introduces an efficient warm-start solution to improve the computational efficiency.…”

Section: Computation Using Alternative Minimizationmentioning

confidence: 99%

“…For example, it takes 52 iterations and about 37.8s to produce Figure (d). In our implementation, the accelerated iterative shrinkage algorithm [BYA13] based on first order proximal operators [PB14] introduces an efficient warm‐start solution to improve the computational efficiency. Please refer to the paper [BYA13] for the accelerated iterative shrinkage algorithm.…”

Section: Volume Denoising / Smoothing Using L0 Gradient Minimizationmentioning

confidence: 99%

“…In our implementation, the accelerated iterative shrinkage algorithm [BYA13] based on first order proximal operators [PB14] introduces an efficient warm‐start solution to improve the computational efficiency. Please refer to the paper [BYA13] for the accelerated iterative shrinkage algorithm. To produce the similar result of Figure (d), the computation time is 3.95s with this acceleration, nearly 9 times faster than the original alternating optimization method.…”

Section: Volume Denoising / Smoothing Using L0 Gradient Minimizationmentioning

confidence: 99%

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Edge‐Aware Volume Smoothing Using L₀ Gradient Minimization

Wang

Tao

Lin

2015

Computer Graphics Forum

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In volume visualization, noise in regions of homogeneous material and at boundaries between different materials poses a great challenge in extracting, analyzing and rendering features of interest. In this paper, we present a novel volume denoising / smoothing method based on the L 0 gradient minimization framework. This framework globally controls how many voxels with a non-zero gradient are in the result in order to approximate important features' structures in a sparse way. This procedure can be solved quickly by the alternating optimization strategy with half-quadratic splitting. While the proposed L 0 volume gradient minimization method can effectively remove noise in homogeneous materials, a blurring-sharpening strategy is proposed to diminish noise or smooth local details on the boundaries. This generates salient features with smooth boundaries and visually pleasing structures. We compare our method with the bilateral filter and anisotropic diffusion, and demonstrate the effectiveness and efficiency of our method with several volumes in different modalities.

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“…Weighted Least Squares (WLS) [8] based multi-scale decomposition algorithm decomposes an image to two layers by solving a weighted least square optimization problem. In [13], an accelerated iterative shrinkage algorithm is proposed to decompose and enhance image. Sparse models usually can give better result in image processing algorithms [9], [14].…”

Section: Introductionmentioning

confidence: 99%

Content Adaptive Image Detail Enhancement

Kou

Chen

et al. 2015

IEEE Signal Process. Lett.

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Detail enhancement is required by many problems in the fields of image processing and computational photography. Existing detail enhancement algorithms first decompose a source image into a base layer and a detail layer via an edge-preserving smoothing algorithm, and then amplify the detail layer to produce a detail-enhanced image. In this letter, we propose a new norm based detail enhancement algorithm which generates the detail-enhanced image directly. The proposed algorithm preserves sharp edges better than an existing norm based algorithm. Experimental results show that the proposed algorithm reduces color distortion in the detail-enhanced image, especially around sharp edges.

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Fast multi-scale detail decomposition via accelerated iterative shrinkage

Cited by 11 publications

References 16 publications

Real-time Image Smoothing via Iterative Least Squares

Real-time Image Smoothing via Iterative Least Squares

Edge‐Aware Volume Smoothing Using L₀ Gradient Minimization

Content Adaptive Image Detail Enhancement

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Fast multi-scale detail decomposition via accelerated iterative shrinkage

Cited by 11 publications

References 16 publications

Real-time Image Smoothing via Iterative Least Squares

Real-time Image Smoothing via Iterative Least Squares

Edge‐Aware Volume Smoothing Using L0 Gradient Minimization

Content Adaptive Image Detail Enhancement

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Edge‐Aware Volume Smoothing Using L₀ Gradient Minimization