Random impulse noise removal using sparse and low rank decomposition of annihilating filter-based Hankel matrix

Author(s):  
Kyong Hwan Jin ◽  
Jong Chul Ye
2018 ◽  
Vol 7 (4) ◽  
pp. 2309
Author(s):  
Baby Victoria.L ◽  
Sathappan S

Noise removal from the color images is the most significant and challenging task in image processing. Among different conventional filter methods, a robust Annihilating filter-based Low-rank Hankel matrix (r-ALOHA) approach was proposed as an impulse noise removal algorithm that uses the sparse and low-rank decomposition of a Hankel structured matrix to decompose the sparse impulse noise components from an original image. However, in this algorithm, the patch image was considered as it was sparse in the Fourier domain only. It requires an analysis of noise removal performance by considering the other transform domains. Hence in this article, the r-ALOHA can be extended into other transform domains such as log and exponential. In the log and exponential domain, the logarithmic and exponential functions are used for modeling the multiplicative noise model. But, this model is used only for positive outcomes. Therefore, wavelet transform domain is applied to the noise model that localizes an image pixel in both frequency and time domain simultaneously. Moreover, it separates the most vital information in a given image. Thus, it is feasible for obtaining a better approximation of the considered function using few coefficients. Finally, the experimental results show the performance effectiveness of the proposed algorithm.  


2015 ◽  
Vol 24 (5) ◽  
pp. 1485-1496 ◽  
Author(s):  
Ruixuang Wang ◽  
Markus Pakleppa ◽  
Emanuele Trucco

2016 ◽  
Vol 78 (1) ◽  
pp. 327-340 ◽  
Author(s):  
Kyong Hwan Jin ◽  
Ji-Yong Um ◽  
Dongwook Lee ◽  
Juyoung Lee ◽  
Sung-Hong Park ◽  
...  

2021 ◽  
Vol 7 (12) ◽  
pp. 279
Author(s):  
Jobin Francis ◽  
Baburaj Madathil ◽  
Sudhish N. George ◽  
Sony George

The massive generation of data, which includes images and videos, has made data management, analysis, information extraction difficult in recent years. To gather relevant information, this large amount of data needs to be grouped. Real-life data may be noise corrupted during data collection or transmission, and the majority of them are unlabeled, allowing for the use of robust unsupervised clustering techniques. Traditional clustering techniques, which vectorize the images, are unable to keep the geometrical structure of the images. Hence, a robust tensor-based submodule clustering method based on l12 regularization with improved clustering capability is formulated. The l12 induced tensor nuclear norm (TNN), integrated into the proposed method, offers better low rankness while retaining the self-expressiveness property of submodules. Unlike existing methods, the proposed method employs a simultaneous noise removal technique by twisting the lateral image slices of the input data tensor into frontal slices and eliminates the noise content in each image, using the principles of the sparse and low rank decomposition technique. Experiments are carried out over three datasets with varying amounts of sparse, Gaussian and salt and pepper noise. The experimental results demonstrate the superior performance of the proposed method over the existing state-of-the-art methods.


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