Video Denoising with Optical Flow Estimation

Buades, Antoni; Lisani, José Luis

doi:10.5201/ipol.2018.224

Cited by 8 publications

(7 citation statements)

References 20 publications

(27 reference 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%

“…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%

A new image denoising model utilizing the conformable fractional calculus for multiplicative noise

Ibrahim¹

2019

SN Appl. Sci.

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Reducing noise from images is an essential structure of the image processing study. Noises can arise with images through achievement on diffusion. The existence of noise can delay the right operation of these images for many applications such as satellite and medical images. Reducing denois in images multiplicatively (DIM) has been developed and modified by many researchers during the past few years. DIM can destroy almost all data of the original image, especially the texture of images. Our aim is to present a new technique to solve this problem. The technique is based on a new fractional calculus called the conformable fractional calculus (CFC). This type of calculus has advantages because of its formula involves a controller, which can be applied to complex problems such as DIM. The proposed structures of CFC windows are given by four masks suggested for x and y directions. On four directional angles, a convolution operational product of the input image pixels with a CFC mask window has been completed. The visual observation and peak signal-to-noise ratio with Root Mean Square Error are employed for measurements. The experiments showed that the skillful filtering outcomes are indicated high score than some well known filers such as Gaussian filter, Sobel edge filter, Canny edge filter and gray-level co-occurrence matrix. Compering is illustrated as well with newly researches.

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“…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%

Alternate Structural-Textural Video Inpainting for Spot Defects Correction in Movies

Renaudeau

Lauze

Pierre

et al. 2019

Lecture Notes in Computer Science

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We propose a new video inpainting model for movies restoration application. Our model combines structural reconstruction with a diffusion-based method and textural reconstruction with a patch-based method. Both proposed energies (one for each method) are alternatively minimized in order to preserve the overall structure while adding textural refinement. While the structural reconstruction is obtained jointly with optical flow computation with several proximal approaches, the textural reconstruction is processed by a variational non-local approach. Preliminary results on different Middlebury frames show quality improvement in the reconstruction.

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“…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%

Video Enhancement Techniques Employed Over the Decade for Reliable Communication

Reddy¹,

Vinayakamurthy²,

Barath³

2019

IJITEE

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Traditional networks are designed to meet the needs of streaming video on the Internet. Relatively high standards of broadband connections and video broadband services make the best effort for certain specific video information and transmission methods to appear at any time to reduce network constraints. Video quality is directly related to the basic ability of the phone to send and receive data from time to time for playback. Record videos from your mobile phone are facing various challenges that have been overcome by a variety of methods. In an extremely robust and unpredictable environment, transversal hardware provides adequate video. Conventional networks are designed to meet the needs of streaming video over the Internet. Relatively high standards of broadband connections and video broadband services make the best effort for certain specific video information and transmission methods to appear at any time to reduce network constraints. Video quality is directly related to the basic ability of the phone to send and receive data from time to time for playback. Record videos from your mobile phone are facing various challenges that have been overcome by a variety of methods. In an extremely robust and unpredictable environment, transversal hardware provides adequate video.

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“…Several works have been published to improve the optical flow computation in noisy images (Spies and Scharr, 2001;Scharr and Spies, 2005), which is one of the main challenges nowadays. Besides, the current literature about video denoising assumes that the motion estimation between frames is an advantage (Liu and Freeman, 2010;Buades et al, 2016), without exploring its influence in depth.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Optical Flow Quality for Video Denoising

Monzón

Sánchez

2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Video denoising techniques need to understand the motion present in the scenes. In the literature, many strategies guide their temporal filters according to trajectories controlled by optical flow. However, the quality of these flows is rarely investigated. In fact, there are very few studies that compare the behavior of denoising proposals with different optical flow algorithms. In that direction, we analyze several methods and their performance using a general pipeline that reduces the noise through an average of the pixel's trajectories. This ensures that the denoising strongly depends on the optical flow. We also analyze the behavior of the methods at occlusions and illumination changes. The pipeline incorporates a process to get rid of these effects, so that they do not affect the comparison metrics. We are led to propose a ranking of optical flows methods depending on their efficiency for video denoising, that mainly depends on their complexity. 2 DENOISING FRAMEWORK Next, we briefly describe the main features of the denoising framework used to compare the influence of the optical flow methods in noise reduction.

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Video Denoising with Optical Flow Estimation

Cited by 8 publications

References 20 publications

A new image denoising model utilizing the conformable fractional calculus for multiplicative noise

Alternate Structural-Textural Video Inpainting for Spot Defects Correction in Movies

Video Enhancement Techniques Employed Over the Decade for Reliable Communication

Comparison of the Optical Flow Quality for Video Denoising

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