2018
DOI: 10.5201/ipol.2018.224
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Video Denoising with Optical Flow Estimation

Abstract: In this paper we describe the implementation of state-of-the-art video denoising algorithm SPTWO [A. Buades, J.L. Lisani, M. Miladinović, Patch Based Video Denoising with Optical Flow Estimation, IEEE Transactions on Image Processing 25 (6), [2573][2574][2575][2576][2577][2578][2579][2580][2581][2582][2583][2584][2585][2586]. This algorithm, inspired by image fusion techniques, uses motion compensation by regularized optical flow methods, which permits robust patch comparison in spatiotemporal volumes. Groups … Show more

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Cited by 8 publications
(7 citation statements)
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“…To check the strength of the proposed CFC algorithm, we compare the proposed study with other approaches for image denoising given in [39][40][41][42]. Figure 8 illustrates the results of the proposed CFC algorithm with different denoising algorithms for the "Cloud, Cake, Boat, Force, Cathedral", and "Bike" images with noise = 15.…”
Section: Comparisonsmentioning
confidence: 99%
See 1 more Smart Citation
“…To check the strength of the proposed CFC algorithm, we compare the proposed study with other approaches for image denoising given in [39][40][41][42]. Figure 8 illustrates the results of the proposed CFC algorithm with different denoising algorithms for the "Cloud, Cake, Boat, Force, Cathedral", and "Bike" images with noise = 15.…”
Section: Comparisonsmentioning
confidence: 99%
“…In [40,41], the authors generalized non-local means for removing noise in images. While, in [42], the authors regularized and weighted the pixel images by using some optical flow methods ( Fig. 7).…”
Section: Comparisonsmentioning
confidence: 99%
“…In the image inpainting context, the idea of mixing both approaches has already been developed in [14] where diffusion and texture filling are sequentially processed, in [6] where the image is decomposed into cartoon and texture before being filled separately, and in [9] where texture is filled guided by the level lines. In the video denoising context, [5] uses patches combined by the computation of a structural optical flow of [23]. Our aim in this paper is also to get the best of both worlds by combining diffusion to recover structure with patches for texture, with also diffusion-based and patch-based approaches for motion estimation.…”
Section: Related Workmentioning
confidence: 99%
“…Pablo Arias, Jean-Michel Morel [17] has empirical Bayesian algorithms for noise reduction based on garments, which means that spatial time adjustments are collected independently and distributed the same samples from the previous distribution. Antoni Buades and others [18] have developed noise analyzes. The image series combines a patch based on dynamic test algorithms and noise reduction.…”
Section: F Deblurring and Denoisingmentioning
confidence: 99%