Decision-Based Marginal Diffusion for Salt-and-Pepper Noise Removal in Color Images

2020 ◽  
pp. 2150153
Author(s):  
Hongyao Deng ◽  
Xiuli Song ◽  
Huilian Fan
Keyword(s):  
Anisotropic Diffusion ◽  
Noise Suppression ◽  
Impulsive Noise ◽  
Geometric Analysis ◽  
Noise Removal ◽  
Diffusion Method ◽  
Salt And Pepper Noise ◽  
The Arts ◽  
Independent Distribution ◽  
Salt And Pepper

Salt-and-pepper noise suppression for vector-valued images usually employs vector median filtering, total variation L1 model, diffusion methods and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and are suitable only for images with low intensity noise. In this paper, a new method, as an important preprocessing step in cyber-physical systems, is presented to suppress salt-and-pepper noise that can overcomes this limitation. This method first detects the corrupted pixels and then restores them using channel-wise anisotropic diffusion. The means is twofold. On the one hand, the marginal approach is used to perform noise suppression separately in each channel because the contaminative pixel components are of independent distribution. On the other hand, a decision-based anisotropic diffusion method is applied to each channel to restores them. The anisotropic diffusion is an energy-dissipating process with time, and dependent on geometric analysis of shape of the energy surface. Simulation results indicate that the proposed method for impulsive noise removal achieves the state-of-the-arts results.

scholarly journals Image Recovery with Data Missing in the Presence of Salt-and-Pepper Noise

2019 ◽  
Vol 9 (7) ◽  
pp. 1426
Author(s):  
Hongqing Liu ◽  
Liming Hou ◽  
Zhen Luo ◽  
Yi Zhou ◽  
Xiaorong Jing ◽  
...  
Keyword(s):  
Noise Suppression ◽  
Impulsive Noise ◽  
Subjective Evaluation ◽  
Estimation Algorithm ◽  
Joint Estimation ◽  
Image Recovery ◽  
Salt And Pepper Noise ◽  
Degrade Image Quality ◽  
Data Missing ◽  
Salt And Pepper

In this paper, an image recovery problem under the case of salt-and-pepper noise and data missing that degrade image quality is addressed if they are not effectively handled, where the salt-and-pepper noise as the impulsive noise is remodeled as a sparse signal due to its impulsiveness and the data missing pattern, denoted by a sparse vector, contains only zeros and ones to formulate the data missing. In particular, the salt-and-pepper noise and data missing are reformatted by their sparsity, respectively. The wavelet and framelet domains are explored to sparsely represent the image in order to accurately reconstruct the clean image. From the reformulations conducted and to recover the image, under one optimization framework, a joint estimation is developed to perform the image recovery, the salt-and-pepper noise suppression, and the missing patter estimation. To solve the optimization problem, two efficient solvers are developed to obtain the joint estimation solution, and they are based on the alternating direction method of multipliers (ADMM) and accelerated proximal gradient (APG). Finally, numerical studies verify that the joint estimation algorithm outperforms the state-of-the-art approaches in terms of both objective and subjective evaluation standards.

scholarly journals A Decision-Based Modified Total Variation Diffusion Method for Impulse Noise Removal

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Hongyao Deng ◽  
Qingxin Zhu ◽  
Xiuli Song ◽  
Jinsong Tao
Keyword(s):  
Total Variation ◽  
Impulse Noise ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Diffusion Method ◽  
Median Filtering ◽  
Noise Characteristics ◽  
Weighted Total Variation ◽  
Salt And Pepper

Impulsive noise removal usually employs median filtering, switching median filtering, the total variation L1 method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted. Pixels falling into different categories are processed differently. If a pixel is corrupted, modified total variation diffusion is applied; if the pixel is possibly corrupted, weighted total variation diffusion is applied; otherwise, the pixel is left unchanged. Experimental results show that the proposed method is robust to different noise strengths and suitable for different images, with strong noise removal capability as shown by PSNR/SSIM results as well as the visual quality of restored images.

scholarly journals Impulsive Noise Removal with an Adaptive Weighted Arithmetic Mean Operator for Any Noise Density

2021 ◽  
Vol 11 (2) ◽  
pp. 560
Author(s):  
Manuel González-Hidalgo ◽  
Sebastia Massanet ◽  
Arnau Mir ◽  
Daniel Ruiz-Aguilera
Keyword(s):  
State Of The Art ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Arithmetic Mean ◽  
Salt And Pepper Noise ◽  
Weighted Arithmetic ◽  
Noise Density ◽  
Salt And Pepper ◽  
Robust To Noise ◽  
Restoration Measures

Many computer vision algorithms which are not robust to noise incorporate a noise removal stage in their workflow to avoid distortions in the final result. In the last decade, many filters for salt-and-pepper noise removal have been proposed. In this paper, a novel filter based on the weighted arithmetic mean aggregation function and the fuzzy mathematical morphology is proposed. The performance of the proposed filter is highly competitive when compared with other state-of-the-art filters regardless of the amount of salt-and-pepper noise present in the image, achieving notable results for any noise density from 5% to 98%. A statistical analysis based on some objective restoration measures supports that this filter surpasses several state-of-the-art filters for most of the noise levels considered in the comparison experiments.

Salt-and-Pepper Noise Removal Based on Image Patch Reordering

2014 ◽  
Vol 701-702 ◽  
pp. 352-356
Author(s):  
Xiong Liang Wang ◽  
Chun Ling Wang
Keyword(s):  
Quantitative Assessment ◽  
Noise Suppression ◽  
Noise Removal ◽  
Experimental Results ◽  
New Method ◽  
Salt And Pepper Noise ◽  
Image Patch ◽  
Qualitative And Quantitative ◽  
Salt And Pepper

A new method based on image patch reordering for removing salt-and-pepper noise from corrupted images is presented. Firstly, the problem of salt-and-pepper noise removal can be turned into the problem of image in-painting. Then, we can use the image patch reordering method to recover the missing pixels and fulfill the salt-and-pepper noise removal. Experimental results demonstrate that the proposed method obtain much better performance in terms of both qualitative and quantitative assessment. Especially, the proposed method provides the improvement in the performance of noise suppression and detail preservation.

scholarly journals On the properties of some adaptive morphological filters for salt and pepper noise removal

2021 ◽  
Author(s):  
Marisol Mares-Javier ◽  
Carlos Guillén-Galván ◽  
Rafael Lemuz-López ◽  
Johan Debayle
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Noise Level ◽  
State Of The Art ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Morphological Filters ◽  
Salt And Pepper

Mathematical Morphology (MM) is a tool that can be applied to many digital image processing tasks that include the reduction of impulsive or salt and pepper noise in grayscale images. The morphological filters used for this task are filters resulting from two basic operators: erosion and dilation. However, when the level of contamination of the image is higher, these filters tend to distort the image. In this work we propose a pair of operators with properties, that better adapt to impulsive noise than other classical morphological filters, it is demonstrated to be increasing idempotent morphological filters. Furthermore, the proposed pair turns out to be a Ʌ-filter and a V-filter which allow to build morphological openings and closings. Finally, they are compared with other filters of the state-of-the-art such as: SMF, PMSF, DBAIN, AMF and NAFSM, and have shown a better performance when the noise level is above 50%.

scholarly journals AN FPGA IMPLEMENTATION OF MODIFIED DECISION BASED UNSYMMETRICAL TRIMMED MEDIAN FILTER FOR THE REMOVAL OF SALT AND PEPPER NOISE IN DIGITAL IMAGES

2014 ◽  
pp. 159-164
Author(s):  
CHAITANYA BETHINA ◽  
M. PREMKUMAR
Keyword(s):  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Noise Removal ◽  
Mean Value ◽  
Low Noise ◽  
Color Images ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Salt And Pepper

A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. Images are often corrupted by impulse noise during acquisition and transmission; thus, an efficient noise suppression technique is required before subsequent image processing operations. Median filter (MF) is widely used in noise removal methods due to its denoising capability and computational efficiency. However, it is effective only for low noise densities. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than denoising techniques. Especially, the proposed method can preserve edges very well while removing salt and pepper noise. Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF) shows better results at low and medium noise densities. At high noise densities, their performance is poor. A new algorithm to remove high-density salt and pepper noise using modified Decision Based Unsymmetric Trimmed Median Filter (DBUTMF) is proposed. The proposed algorithm replaces the noisy pixel by trimmed median. Since our algorithm is algorithmically simple, it is very suitable to be applied to many real-time applications and higher noise densities. When all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

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