Compressive Bilateral Filtering

Sugimoto, Kenjiro; Kamata, Sei‐ichiro

doi:10.1109/tip.2015.2442916

Cited by 74 publications

(95 citation statements)

References 49 publications

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“…Similarly to [4,11,2,7] the present idea is to use trigonometric sums for approximating the range kernel. The key difference is that instead of approximating the continuous kernel, we propose to approximate the discrete kernel samples.…”

Section: Proposed Algorithmmentioning

confidence: 99%

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A Fast Approximation of the Bilateral Filter using the Discrete Fourier Transform

Nair¹,

Popli²,

Chaudhury³

2017

Image Processing on Line

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The bilateral filter is a popular non-linear smoother that has applications in image processing, computer vision, and computational photography. The novelty of the filter is that a range kernel is used in tandem with a spatial kernel for performing edge-preserving smoothing, where both kernels are usually Gaussian. A direct implementation of the bilateral filter is computationally expensive, and several fast approximations have been proposed to address this problem. In particular, it was recently demonstrated in a series of papers that a fast and accurate approximation of the bilateral filter can be obtained by approximating the Gaussian range kernel using polynomials and trigonometric functions. By adopting some of the ideas from this line of work, we propose a fast algorithm based on the discrete Fourier transform of the samples of the range kernel. We develop a parallel C implementation of the resulting algorithm for Gaussian kernels, and analyze the effect of various extrinsic and intrinsic parameters on the approximation quality and the run time. A key component of the implementation are the recursive Gaussian filters of Deriche and Young. Source CodeThe ANSI C source code used in the demo can be downloaded from the web page of this article 1 . Compilation and usage instruction are included in the README.txt of the archive.

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Section: Proposed Algorithmmentioning

confidence: 99%

“…Several fast algorithms have been proposed that offer various trade-offs between speed and accuracy [6,8,10,13,4,11,3,1,2,7]. We refer the interested reader to these recent papers [11,3] for a survey and comparison of various fast algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Fast Approximation of the Bilateral Filter using the Discrete Fourier Transform

Nair¹,

Popli²,

Chaudhury³

2017

Image Processing on Line

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“…알고리즘 [6] , 임의의 공간 및 영역 윈도우를 분해한 뒤 여러 개의 공간 필터를 사용하여 양자화와 보간을 하 는 Durand와 Dorsey의 알고리즘 [4] , 푸리에 분석을 사 용하여 가우시안 영역 윈도우를 추정하고 주기를 최 적화 한 Sugimoto 등의 압축 알고리즘 [7] 등이 제안되 었다.…”

Section: ⅰ 서 론 이미지 필터링은 영상 처리와 컴퓨터 비전 분야에 서 자주 사용되는 처리 과정이며 기본 단위인 필unclassified

Hardware Design of Bilateral Filter Based on Window Division

Hyun¹,

Park²

2016

The Journal of Korean Institute of Communications and Informati

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The bilateral filter can reduce the noise while preserving details computing the filtering output at each pixels as the average of neighboring pixels. In this paper, we propose a real-time system based on window division.Overall performance is increased due to the parallel architectures which computes five rows in the kernel window simultaneously but with pipelined scheduling. We consider the tradeoff between the filter performance and the hardware cost and the bit allocation has been determined by PSNR analysis. The proposed architecture is designed with verilogHDL and synthesized using Dongbu Hitek 110nm standard cell library. The proposed architecture shows 416Mpixels/s (397fps) of throughput at 416MHz of operating frequency with 132K gates.

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“…The BF enables us to smooth an image while preserving edges and textures by using filter weights determined from both spatial kernel (pixel position) and range kernel (pixel intensity). Following the original work, many improved methods have been actively proposed to further enhance smoothing quality [4,5] and to reduce computational complexity [7,8,[13][14][15][16][17][18][19][20][21]. A major drawback of the original BF is the computational complexity depending on its filter window size.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, the original BF is approximated by an appropriate combination of a series of convolutions, (e.g. by splatting/slicing technique [8,15,16], histogram technique [14] or range kernel decomposition [20,21]). Secondly, each convolution is operated by an O(1) method, (e.g.…”

Section: Introductionmentioning

confidence: 99%

Constant-time bilateral filter using spectral decomposition

Sugimoto

Breckon

Kamata

2016

2016 IEEE International Conference on Image Processing (ICIP)

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any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTThis paper presents an efficient constant-time bilateral filter where constant-time means that computational complexity is independent of filter window size. Many state-of-the-art constant-time methods approximate the original bilateral filter by an appropriate combination of a series of convolutions. It is important for this framework to optimize the performance tradeoff between approximate accuracy and the number of convolutions. The proposed method achieves the optimal performance tradeoff in a least-squares manner by using spectral decomposition under the assumption that images consist of discrete intensities such as 8-bit images. This approach is essentially applicable to arbitrary range kernel. Experiments show that the proposed method outperforms state-of-the-art methods in terms of both computational complexity and approximate accuracy.

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Compressive Bilateral Filtering

Cited by 74 publications

References 49 publications

A Fast Approximation of the Bilateral Filter using the Discrete Fourier Transform

A Fast Approximation of the Bilateral Filter using the Discrete Fourier Transform

Hardware Design of Bilateral Filter Based on Window Division

Constant-time bilateral filter using spectral decomposition

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