Fast Non-Local algorithm for image denoising

Karnati, Venkateswarlu; Uliyar, Mithun; Dey, Sumit

doi:10.1109/icip.2009.5414044

Cited by 70 publications

(34 citation statements)

References 8 publications

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“…Another speed enhancement, based on probabilistic early termination, is proposed in [12]. Karnati et al [13] proposed a multiresolution approach requiring fewer comparisons.…”

Section: Introductionmentioning

confidence: 99%

Recursive non-local means filter for video denoising

Ali

Hardie

2017

J Image Video Proc.

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In this paper, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on non-local means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patches, within a given search window. The weights are based on the patch intensity vector distances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video processing, can be computationally demanding. Note that the size of a 3D search window is the size of the 2D search window multiplied by the number of frames being used to form the output. Exploiting a large number of frames in this manner can be prohibitive for real-time video processing. Here, we propose a novel recursive NLM (RNLM) algorithm for video processing. Our RNLM method takes advantage of recursion for computational savings, compared with the direct 3D NLM. However, like the 3D NLM, our method is still able to exploit both spatial and temporal redundancy for improved performance, compared with 2D NLM. In our approach, the first frame is processed with single-frame NLM. Subsequent frames are estimated using a weighted sum of pixels from the current frame and a pixel from the previous frame estimate. Only the single best matching patch from the previous estimate is incorporated into the current estimate. Several experimental results are presented here to demonstrate the efficacy of our proposed method in terms of quantitative and subjective image quality.

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“…Another speed enhancement, based on probabilistic early termination, is proposed in [12]. Karnati et al [13] proposed a multiresolution approach requiring fewer comparisons.…”

Section: Introductionmentioning

confidence: 99%

Recursive non-local means filter for video denoising

Ali

Hardie

2017

J Image Video Proc.

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“…The dimension-reduction techniques include principle component analysis (10,11,(13)(14)(15) and singular value decomposition (12) . The third category is to speed up the computation by using either efficient algorithms or data structures (16)(17)(18)(19)(20)(21)(22)(23) . The efficient algorithms being used are random selections (16,23) , fast Fourier transform (17,18,22) , squared image (18,19) , and probabilistic early termination (21) .…”

Section: Introductionmentioning

confidence: 99%

“…The data structures for improving efficiency include Laplacian pyramid (18) and multiresolution representations (20) .…”

Section: Introductionmentioning

confidence: 99%

Efficient Non-Local Image Denoising Using Binary Descriptor Classification

Liao

Tsai

Huang

2016

The Proceedings of the 4th International Conference on Industrial Application Engineering 2016

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Non-local mean (NLM) is one of the most effective image denoising methods currently available. It calculates weights of neighboring pixels based on the similarity between two image patches. The pixel is then estimated by the weighted sum of the neighboring pixels and itself. Because the large number of the patches needs to be compared, NLM incurs extremely high computational cost. In this paper, we propose to use binary descriptors to reject dissimilar patches from the computation and improve the performance of NLM. A binary descriptor of a patch compares the pixels in the patch with a given threshold and produces a simple binary string to describe the patch. It is very simple to generate and the comparison between two descriptors is computationally efficient. Experimental results show that using a simple binary descriptor can effectively increase the denoising performance of NLM and significantly reduce the execution time.

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“…The second category is to simplify patch comparison by reducing the dimensions of the image patches (10)(11)(12)(13)(14)(15) by using principle component analysis (10,11,(13)(14)(15) and singular value decomposition (12) . The third category is to accelerate the computation through either efficient algorithms or data structures (16)(17)(18)(19)(20)(21)(22)(23) . The efficient algorithms being used are random selections (16,23) , fast Fourier transform (17,18,22) , squared image (18,19) , and probabilistic early termination (21) .…”

Section: Introductionmentioning

confidence: 99%

“…The efficient algorithms being used are random selections (16,23) , fast Fourier transform (17,18,22) , squared image (18,19) , and probabilistic early termination (21) . The data structures for improving efficiency include Laplacian pyramid (18) and multiresolution representations (20) . Among these three categories of acceleration techniques, the first category has an additional advantage of producing images with less blurring.…”

Section: Introductionmentioning

confidence: 99%

Optimal Binary Descriptor for Pre-Classification in Efficient Non-Local Mean Image Denoising

Liao

Chen

2016

Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing 2016

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With the popularity of consumer digital imaging devices, image denoising has become an important issue in image processing. Among currently available image denoising algorithms, non-local mean (NLM) is one of the most effective methods. NLM calculates weights of neighboring pixels based on the similarity between two image patches. The pixel is then estimated by the weighted sum of the neighboring pixels. Because the large number of the patches needs to be compared, NLM incurs high computational cost and this makes it impractical in many situations. On the other hand, a binary descriptor produces a simple binary string to describe an image patch by comparing pixels with a given threshold. Comparing binary descriptors between two image patches is much more efficient than comparing the two patches directly. Preliminary results show that using binary descriptors to reject dissimilar patches from the computation can significantly improve the performance of NLM. In this paper, we seek to find the best binary descriptor which can provide large speedup and enhance denosing performance. Experimental results show that using two types of 18-neighbor binary descriptors can effectively increase the denoising performance of NLM and significantly reduce the execution time.

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Fast Non-Local algorithm for image denoising

Cited by 70 publications

References 8 publications

Recursive non-local means filter for video denoising

Recursive non-local means filter for video denoising

Efficient Non-Local Image Denoising Using Binary Descriptor Classification

Optimal Binary Descriptor for Pre-Classification in Efficient Non-Local Mean Image Denoising

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