scholarly journals Adaptive video denoising using block matching 3-D filtering

2012 ◽  
Author(s):  
Guangyi Chen ◽  
T. D. Bui ◽  
A. Krzyzak ◽  
S. Coulombe
Keyword(s):  
Block Matching ◽  
Video Denoising ◽  
Adaptive Video

Video denoising algorithm based on improved dual-domain filtering and 3D block matching

2018 ◽  
Vol 12 (12) ◽  
pp. 2250-2257 ◽  
Author(s):  
Jinsheng Xiao ◽  
Wentao Zou ◽  
Shangyue Zhang ◽  
Junfeng Lei ◽  
Wen Wang ◽  
...  
Keyword(s):  
Block Matching ◽  
Video Denoising ◽  
Dual Domain

scholarly journals Video Denoising Based on a Spatiotemporal Kalman-Bilateral Mixture Model

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Chenglin Zuo ◽  
Yu Liu ◽  
Xin Tan ◽  
Wei Wang ◽  
Maojun Zhang
Keyword(s):  
Mixture Model ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Bilateral Filter ◽  
Block Matching ◽  
Low Light ◽  
Denoising Method ◽  
Video Denoising ◽  
Temporal Domain ◽  
Video Frames

We propose a video denoising method based on a spatiotemporal Kalman-bilateral mixture model to reduce the noise in video sequences that are captured with low light. To take full advantage of the strong spatiotemporal correlations of neighboring frames, motion estimation is first performed on video frames consisting of previously denoised frames and the current noisy frame by using block-matching method. Then, current noisy frame is processed in temporal domain and spatial domain by using Kalman filter and bilateral filter, respectively. Finally, by weighting the denoised frames from Kalman filtering and bilateral filtering, we can obtain a satisfactory result. Experimental results show that the performance of our proposed method is competitive when compared with state-of-the-art video denoising algorithms based on both peak signal-to-noise-ratio and structural similarity evaluations.

scholarly journals Iterative based image and video denoising by fractional block matching and transform domain filtering

2021 ◽  
Author(s):  
Amir Mehdizadeh Hemat Abadi ◽  
Mohammad Reza Hosseiny Fatemi
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Weighted Average ◽  
Block Matching ◽  
Objective Measures ◽  
Transform Domain ◽  
Video Denoising ◽  
Simulation Results ◽  
Fractional Motion Estimation

This paper presents an iterative algorithm for image and video denoising which is based on fractional block-matching and transform domain filtering. We propose fractional motion estimation technique to find the most accurate similar blocks for each block of an image which improves sparsity enabling effective image denoising. By taking the advantage of blocks similarity and wavelet transform domain filtering along with weighted average function (WAF) in an iterative based manner, we achieve a higher level of sparsity and a better exploiting of blocks similarity redundancies of noisy images that increase the chance of preserving details and edges in the restored image. Since our algorithm is iterative, we can tradeoff between image denoising degree and computational complexity. In addition, we develop a video denoising algorithm based on the proposed image denoising algorithm. The simulation results of images and videos contaminated by additive white Gaussian noise demonstrate that our algorithm substantially achieves better denoising performance compared with previously published algorithms in terms of subjective and objective measures.

scholarly journals Spatiotemporal Video Denoising Based on Adaptive Thresholding and Clustering

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Ali Abdullah Yahya ◽  
Jieqing Tan ◽  
Benyu Su ◽  
Kui Liu ◽  
Ali Naser Hadi
Keyword(s):  
Signal To Noise Ratio ◽  
Computation Time ◽  
Block Matching ◽  
Adaptive Thresholding ◽  
Hard Thresholding ◽  
Denoising Method ◽  
Video Denoising ◽  
Final Estimate ◽  
Clustering Approach ◽  
Lower Noise

In this paper we propose a novel video denoising method based on adaptive thresholding and K-means clustering. In the proposed method the adaptive thresholding is applied rather than the conventional hard-thresholding of the VBM3D method. The adaptive thresholding has a high ability to adapt and change according to the amount of noise. More specifically, hard-thresholding is applied on the higher noise areas while soft-thresholding is applied on the lower noise areas. Consequently, we can successfully remove the noise effectively and at the same time preserve the edges of the image, because the clustering approach saves more computation time and is more capable of finding relevant patches than the block-matching approach. So, the K-means clustering method in the final estimate in this paper is adopted instead of the block-matching method in the VBM3D method in order to restrict the search of the candidate patches within the region of the reference patch and therefore improve the grouping. Experimental results emphasize the superiority of the new method over the reference methods in terms of visual quality, Peak Signal-to-Noise Ratio (PSNR), and Image Enhancement Factor (IEF). Execution time of the proposed algorithm consumes less time in denoising than that in the VBM3D algorithm.

scholarly journals Iterative based image and video denoising by fractional block matching and transform domain filtering

2021 ◽  
Author(s):  
Amir Mehdizadeh Hemat Abadi ◽  
Mohammad Reza Hosseiny Fatemi
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Weighted Average ◽  
Block Matching ◽  
Objective Measures ◽  
Transform Domain ◽  
Video Denoising ◽  
Simulation Results ◽  
Fractional Motion Estimation

This paper presents an iterative algorithm for image and video denoising which is based on fractional block-matching and transform domain filtering. We propose fractional motion estimation technique to find the most accurate similar blocks for each block of an image which improves sparsity enabling effective image denoising. By taking the advantage of blocks similarity and wavelet transform domain filtering along with weighted average function (WAF) in an iterative based manner, we achieve a higher level of sparsity and a better exploiting of blocks similarity redundancies of noisy images that increase the chance of preserving details and edges in the restored image. Since our algorithm is iterative, we can tradeoff between image denoising degree and computational complexity. In addition, we develop a video denoising algorithm based on the proposed image denoising algorithm. The simulation results of images and videos contaminated by additive white Gaussian noise demonstrate that our algorithm substantially achieves better denoising performance compared with previously published algorithms in terms of subjective and objective measures.

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