Mixed noise removal using cellular automata and Gaussian scale mixture in digital image

2011 ◽  
Author(s):  
Jiayou Liu ◽  
Kequan Lin
Keyword(s):  
Cellular Automata ◽  
Digital Image ◽  
Noise Removal ◽  
Scale Mixture ◽  
Gaussian Scale Mixture ◽  
Mixed Noise ◽  
Mixed Noise Removal

Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-Rank Approximation

2017 ◽  
Vol 26 (7) ◽  
pp. 3171-3186 ◽  
Author(s):  
Tao Huang ◽  
Weisheng Dong ◽  
Xuemei Xie ◽  
Guangming Shi ◽  
Xiang Bai
Keyword(s):  
Noise Removal ◽  
Mixture Modeling ◽  
Low Rank ◽  
Scale Mixture ◽  
Low Rank Approximation ◽  
Mixed Noise ◽  
Rank Approximation ◽  
Mixed Noise Removal

scholarly journals Smart Switching Bilateral Filter with Estimated Noise Characterization for Mixed Noise Removal

2019 ◽  
Vol 2019 ◽  
pp. 1-23
Author(s):  
Kriengkri Langampol ◽  
Kanabadee Srisomboon ◽  
Vorapoj Patanavijit ◽  
Wilaiporn Lee
Keyword(s):  
Communication Channel ◽  
Bilateral Filter ◽  
Noise Removal ◽  
Block Matching ◽  
Trilateral Filter ◽  
Optimal Value ◽  
Spatial Weight ◽  
Mixed Noise ◽  
Mixed Noise Removal ◽  
Noise Characterization

Traditionally, several existing filters are proposed for removing a specific type of noise. However, in practice, the image communicated through the communication channel may be contaminated with more than one type of noise. Switching bilateral filter (SBF) is proposed for removing mixed noise by detecting a contaminated noise at the concerned pixel and recalculates the filter parameters. Although the filter parameters of SBF are sensitive to type and strength of noise, the traditional SBF filter has not taken the strength into account. Therefore, the traditional SBF filter cannot remove the mixed noise efficiently. In this paper, we propose a smart switching bilateral filter (SSBF) to outperform a demerit of traditional SBF filter. In the first stage of SSBF, we propose a new scheme of noise estimation using domain weight (DW) pattern which characterizes the distribution of the different intensity between a considered pixel and its neighbors. By using this estimation, the types of mixed noises and their strength are estimated accurately. The filter parameters of SBF are selected from the table where the spatial weight and radiometric weight are already learned. As a result, SSBF can improve the performance of traditional SBF and can remove mixed noises efficiently without knowing the exact type of contaminated mixed noise. Moreover, the performance of SSBF is compared to the optimal SBF filter (OSBF) where OSBF sets the optimal value of filter parameters on the contaminated mixed noise and three new filters — block-matching and 3D filtering (BM3D), nonlocal sparse representation (NCSR), and trilateral filter (TF). The simulation results showed that the performance of SSBF outperforms BM3D, NCSR, TF, and SBF and is near to optimal SBF filter, even if the SSBF does not know the type of mixed noise.

An Adaptive Filtering Algorithm Based on Improved Gray Relational Analysis

2011 ◽  
Vol 148-149 ◽  
pp. 483-486
Author(s):  
Chun Yan Huang ◽  
Yan Ling Li
Keyword(s):  
Adaptive Filtering ◽  
Noise Removal ◽  
Image Features ◽  
Gray Relational Analysis ◽  
Filtering Algorithm ◽  
Relational Analysis ◽  
Mean Filter ◽  
Gray Relation Analysis ◽  
Mixed Noise ◽  
Mixed Noise Removal

Because of the characteristic of the gray relation analysis and the advantage of the alpha-trimmed mean filter, an efficient technique for mixed noise removal in images was proposed. This algorithm can adjust the filter coefficients adaptively according to various pieces of the image features. Experiment results show that the proposed algorithm which greatly improved efficiently, it not only can remove mixed noise in image, but also can keep the details of the image.

scholarly journals Infimal Convolution of Data Discrepancies for Mixed Noise Removal

2017 ◽  
Vol 10 (3) ◽  
pp. 1196-1233 ◽  
Author(s):  
Luca Calatroni ◽  
Juan Carlos De Los Reyes ◽  
Carola-Bibiane Schönlieb
Keyword(s):  
Noise Removal ◽  
Infimal Convolution ◽  
Mixed Noise ◽  
Mixed Noise Removal
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