Efficient video denoising based on dynamic nonlocal means

Han, Yubing; Chen, Rushan

doi:10.1016/j.imavis.2012.01.002

Cited by 28 publications

(22 citation statements)

References 23 publications

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“…We compare our method to several state-of-the-art methods including SNLM [3], 3D NLM, BM3D [17], VBM3D [18], and DNLM [16]. Our results make use of standard and publicly available video sequences allowing for reproducible and comparative results.…”

Section: Resultsmentioning

confidence: 99%

“…We acknowledge that the proposed RNLM does not have the optimal framework of the Kalman filter [16]. However, it can exploit temporal signal correlation effectively, as we shall show in Section 4.…”

Section: The Proposed Rnlm Video Denoising Algorithm Definitionmentioning

confidence: 97%

“…In this table, several additional published results are reported for comparison. These include wavelet domain recursive spatio-temporal filtering (WRSTF) [31], sequential wavelet domain and temporal filtering (SEQWT) [32], 3D wavelet transform method (3DWTF) [33], inter-frame statistical modeling (IFSM) [34], 3D sliding window discrete cosine transform (3DSWDCT) [35], VBM3D [18], spatiotemporal Gaussian scale mixture (ST-GSM) [36], and DNLM [16]. The results for these methods are the reported results from the respective papers for the same sequences and noise levels.…”

Section: Mse(k)mentioning

confidence: 99%

“…Among these are the NLM method, which has been applied successfully to image sequences in [14] and [15]. Han et al [16] introduced the dynamic non-local means (DNLM) video denoising method, which is based on Kalman filter theory. The basic idea of this filter is to use information from the past video frames to restore the current frame, combining the NLM and Kalman filtering algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…This is done to maximize the temporal correlation. Our approach shares its recursive nature with DNLM in [16]. However, here, we have opted for a much simpler framework, in keeping with the simplicity of the original NLM.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Resultsmentioning

confidence: 99%

Section: The Proposed Rnlm Video Denoising Algorithm Definitionmentioning

confidence: 97%