scholarly journals A Modified Iterative Alternating Direction Minimization Algorithm for Impulse Noise Removal in Images

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Di Guo ◽  
Xiaobo Qu ◽  
Meng Wu ◽  
Keshou Wu
Keyword(s):  
Impulse Noise ◽  
Noise Removal ◽  
Nonlocal Operator ◽  
Salt And Pepper Noise ◽  
Self Similarity ◽  
Impulse Noise Removal ◽  
Alternating Direction ◽  
Continuation Algorithm ◽  
Simulation Results ◽  
Salt And Pepper

Images are often corrupted by impulse noise. In this paper, an alternating direction minimization with continuation algorithm is modified and iteratively used to remove the impulse noise in images by exploring its self-similarity. A patch-based nonlocal operator and sparse representation are married in thel1-l1optimization model to be solved. Simulation results demonstrate that the proposed algorithm outperforms typical denoising methods in terms of preserving edges and textures for both salt-and-pepper noise and random-valued impulse noise. It can be also applied to suppress impulse noise-like artifacts in real mural images.

A New Detector for Impulse Noise Removal

2013 ◽  
Vol 779-780 ◽  
pp. 1797-1800
Author(s):  
Tian Shou Liang ◽  
De Jian Zhou
Keyword(s):  
Impulse Noise ◽  
Noise Removal ◽  
Gray Level ◽  
Impulse Noise Removal ◽  
Noise Filter ◽  
Simulation Results ◽  
Gray Level Images

This paper proposes a new detector for impulse noise filter on switch scheme. We construct the detector by using two kind of information: the closeness and volatility of intensities of local pixels. The simulation results of noise detecting with gray level images show that the proposed detector has a better performance than other detectors with high correct rate and low misjudgment for impulse noise.

scholarly journals An Alternating Direction Method for Mixed Gaussian Plus Impulse Noise Removal

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Si Wang ◽  
Ting-Zhu Huang ◽  
Xi-le Zhao ◽  
Jun Liu
Keyword(s):  
Total Variation ◽  
Numerical Experiments ◽  
Impulse Noise ◽  
State Of The Art ◽  
Noise Removal ◽  
Alternating Direction Method ◽  
Impulse Noise Removal ◽  
Alternating Direction ◽  
Total Variation Model ◽  
Blurred Images

A combined total variation and high-order total variation model is proposed to restore blurred images corrupted by impulse noise or mixed Gaussian plus impulse noise. We attack the proposed scheme with an alternating direction method of multipliers (ADMM). Numerical experiments demonstrate the efficiency of the proposed method and the performance of the proposed method is competitive with the existing state-of-the-art methods.

scholarly journals Salt and Pepper Noise Removal with Noise Detection and a Patch-Based Sparse Representation

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Di Guo ◽  
Xiaobo Qu ◽  
Xiaofeng Du ◽  
Keshou Wu ◽  
Xuhui Chen
Keyword(s):  
Sparse Representation ◽  
Impulse Noise ◽  
Structural Similarity ◽  
Noise Removal ◽  
Noise Detection ◽  
Noise Levels ◽  
Denoising Method ◽  
Transmission Errors ◽  
Alternating Direction ◽  
Salt And Pepper

Images may be corrupted by salt and pepper impulse noise due to noisy sensors or channel transmission errors. A denoising method by detecting noise candidates and enforcing image sparsity with a patch-based sparse representation is proposed. First, noise candidates are detected and an initial guide image is obtained via an adaptive median filtering; second, a patch-based sparse representation is learnt from this guide image; third, a weightedl1-l1regularization method is proposed to penalize the noise candidates heavier than the rest of pixels. An alternating direction minimization algorithm is derived to solve the regularization model. Experiments are conducted for 30%∼90% impulse noise levels, and the simulation results demonstrate that the proposed method outperforms total variation and Wavelet in terms of preserving edges and structural similarity to the noise-free images.

Salt-and-Pepper Noise Removal via Local Hölder Seminorm and Nonlocal Operator for Natural and Texture Image

2014 ◽  
Vol 51 (3) ◽  
pp. 400-412 ◽  
Author(s):  
Kehan Shi ◽  
Zhichang Guo ◽  
Gang Dong ◽  
Jiebao Sun ◽  
Dazhi Zhang ◽  
...  
Keyword(s):  
Noise Removal ◽  
Nonlocal Operator ◽  
Salt And Pepper Noise ◽  
Texture Image ◽  
Salt And Pepper

scholarly journals Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations

Algorithms ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 7
Author(s):  
Di Guo ◽  
Zhangren Tu ◽  
Jiechao Wang ◽  
Min Xiao ◽  
Xiaofeng Du ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Impulse Noise ◽  
State Of The Art ◽  
Evaluation Criteria ◽  
Objective Evaluation ◽  
Noise Removal ◽  
Alternating Direction ◽  
Data Fidelity ◽  
Salt And Pepper ◽  
Image Details

Images may be corrupted by salt and pepper impulse noise during image acquisitions or transmissions. Although promising denoising performances have been recently obtained with sparse representations, how to restore high-quality images remains challenging and open. In this work, image sparsity is enhanced with a fast multiclass dictionary learning, and then both the sparsity regularization and robust data fidelity are formulated as minimizations of L0-L0 norms for salt and pepper impulse noise removal. Additionally, a numerical algorithm of modified alternating direction minimization is derived to solve the proposed denoising model. Experimental results demonstrate that the proposed method outperforms the compared state-of-the-art ones on preserving image details and achieving higher objective evaluation criteria.

scholarly journals Impulse Noise Removal Based on Hybrid Genetic Algorithm

2021 ◽  
Vol 38 (4) ◽  
pp. 1245-1251
Author(s):  
Nail Alaoui ◽  
Arwa Mashat ◽  
Amel Baha Houda Adamou-Mitiche ◽  
Lahcène Mitiche ◽  
Aicha Djalab ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Hybrid Genetic Algorithm ◽  
Structural Similarity ◽  
Noise Removal ◽  
Impulse Noise Removal ◽  
Salt And Pepper ◽  
Crossover And Mutation ◽  
Different Levels

In this paper, we introduce a new method, impulse noise removal based on hybrid genetic algorithm (INRHGA) to remove impulse noise at different noise densities of noise while preserving the main features of the image. The proposed approach merges the genetic algorithm and methods for filtering images that are combined into the population as essential solutions to create a developed and improved population. A set of individuals is developed into a number of iterations using factors of crossover and mutation. Our method develops a group of images instead of a set of parameters from the filters. We then introduced some of the concepts and steps of it. The proposed algorithm is compared with some image denoising algorithm. By using Peak Signal to Noise Ratio (PSNR), structural similarity (SSIM). For example, for Lenna image with 60% salt and pepper noise density, PSNR, SSIM results of AMF, MDBUTMFG and NAFSM methods are 20,39/ 28.74/ 29.85 and 0.5679/ 0.8312/ 0.8818 respectively, while PSNR, SSIM results of the proposed algorithm are 29.92 and 0.8838, respectively. Experimental results indicate that INRHGA is very effective and visually comparable with the above-mentioned methods at different levels of noise.

Enhancing the Performance of One Dimensional Median Filter

2019 ◽  
Vol 118 (7) ◽  
pp. 73-76
Author(s):  
Sharanabasappa ◽  
P Ravibabu
Keyword(s):  
Impulse Noise ◽  
Rank Order ◽  
Median Filter ◽  
Filter Method ◽  
Salt And Pepper Noise ◽  
Edge Preservation ◽  
Word Level ◽  
Look Ahead ◽  
Filter Architecture ◽  
Salt And Pepper

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter  in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.

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