Novel Speed-Up Strategies for Non-Local Means Denoising With Patch and Edge Patch Based Dictionaries

Bhujle, Hemalata; Chaudhuri, Subhasis

doi:10.1109/tip.2013.2290871

Cited by 44 publications

(13 citation statements)

References 38 publications

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“…In this section, the proposed algorithm is evaluated by comparing with M-BF [3], VBM4D [5], shift-able bilateral Filter (St-BF) [8] and Su-NLM [9]. Reference parameters are: In the first experiment, we compare the proposed algorithm with these state-of-the-art algorithms on synthetic noisy videos.…”

Section: Resultsmentioning

confidence: 99%

Video denoising algorithm via multi-scale joint luma-chroma bilateral filter

Gao

2015

2015 Visual Communications and Image Processing (VCIP)

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Video denoising is important for display and subsequent analysis, but remains to be a challenging problem. Key insights that limit the performance of algorithms include two main aspects. First, low-frequency scene information and the coarse-grained noise in the chroma are mixed with each other, which is different from that in the luma. Thus, denoising the chroma by using only its own information is difficult. Second, it is impossible to directly use the denoised luma edge information to guide the chroma denoising for high quality results. Therefore, in this paper, we propose a multi-scale joint luma-chroma bilateral filter to improve chroma denoising performance. Experimental results demonstrate that the proposed algorithm surpasses state-of-the-art algorithms in most cases.

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Section: Resultsmentioning

confidence: 99%

Video denoising algorithm via multi-scale joint luma-chroma bilateral filter

Gao

2015

2015 Visual Communications and Image Processing (VCIP)

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“…In the vector detection approach, a codebook of representative patches from training data is used, rather than patches from the observed image itself [16,17]. A number of NLM variations have been proposed, including [18][19][20][21][22][23][24][25][26]. The basic NLM method forms an estimate of a reference pixel as a weighted sum of non-local pixels.…”

Section: Image Restorationmentioning

confidence: 99%

A collaborative adaptive Wiener filter for image restoration using a spatial-domain multi-patch correlation model

Mohamed

Hardie

2015

EURASIP J. Adv. Signal Process.

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We present a new patch-based image restoration algorithm using an adaptive Wiener filter (AWF) with a novel spatial-domain multi-patch correlation model. The new filter structure is referred to as a collaborative adaptive Wiener filter (CAWF). The CAWF employs a finite size moving window. At each position, the current observation window represents the reference patch. We identify the most similar patches in the image within a given search window about the reference patch. A single-stage weighted sum of all of the pixels in the similar patches is used to estimate the center pixel in the reference patch. The weights are based on a new multi-patch correlation model that takes into account each pixel's spatial distance to the center of its corresponding patch, as well as the intensity vector distances among the similar patches. One key advantage of the CAWF approach, compared with many other patch-based algorithms, is that it can jointly handle blur and noise. Furthermore, it can also readily treat spatially varying signal and noise statistics. To the best of our knowledge, this is the first multi-patch algorithm to use a single spatial-domain weighted sum of all pixels within multiple similar patches to form its estimate and the first to use a spatial-domain multi-patch correlation model to determine the weights. The experimental results presented show that the proposed method delivers high performance in image restoration in a variety of scenarios.

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“…Bhujle [9] proposed a dictionary based denoising in which patches with similar photometric structures are clustered together to create groups. Here, they build a dictionary prior to denoising which can be accessed at a constant time.…”

Section: Improvement Over Non-local Meansmentioning

confidence: 99%

Improved Non-Local Means Algorithm Based on Dimensionality Reduction

Maruf

El-Sakka

2015

Lecture Notes in Computer Science

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Non-Local Means is an image denoising algorithm based on patch similarity. It compares a reference patch with the neighboring patches to find similar patches. Such similar patches participate in the weighted averaging process. Most of the computational time for Non-LocalMeans is consumed to measure patch similarity. In this thesis, we have proposed an improvement where the image patches are projected into a global feature space. Then we have performed a statistical t-test to reduce the dimensionality of this feature space.Denoising is achieved based on this reduced feature space and the proposed modification exploits an improvement in terms of denoising performance and computational time.

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Novel Speed-Up Strategies for Non-Local Means Denoising With Patch and Edge Patch Based Dictionaries

Cited by 44 publications

References 38 publications

Video denoising algorithm via multi-scale joint luma-chroma bilateral filter

Video denoising algorithm via multi-scale joint luma-chroma bilateral filter

A collaborative adaptive Wiener filter for image restoration using a spatial-domain multi-patch correlation model

Improved Non-Local Means Algorithm Based on Dimensionality Reduction

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