Structured Dictionary Learning for Image Denoising Under Mixed Gaussian and Impulse Noise

With the emerging technology of remote sensing, a huge amount of remote sensing data is collected and stored in the remote sensin02222g platform, and the transmission and processing of data on the platform is extremely wasteful. It is essential to incorporate the speedy remote sensing processing services in an integrated cloud computing architecture. In order to improve the denoising ability of remote sensing image, a new structured dictionary-based method for multispectral image denoising based on cluster is proposed. This method incorporates both the locality of spatial and the correlation across spectrum of multispectral image. Remote sensing image is divided into different groups by clustering, and sparse representation coefficients of spatial and spectral and dictionary is obtained according to the dictionary learning algorithm. After threshold processing, the similar blocks are averaged and realized with multispectral remote sensing image denoising. The algorithm is applied to denoise the noisy remote sensing image of Maoergai area in the upper Minjiang which contain typical vegetation and soil is chosen as study area, simulation results show that higher peak-signal to noise ratio can be obtained as compared to other recent image denoising methods.

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Structured Dictionary Learning with Block Diagonal Regularization for Image Classification

2020 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata50022.2020.9378209 ◽

2020 ◽

Author(s):

Manman Xu ◽

Runhua Jiang ◽

Tao Wang ◽

Di Wang ◽

Xiaoju Lu

Keyword(s):

Image Classification ◽

Dictionary Learning ◽

Structured Dictionary ◽

Block Diagonal

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Directionally-structured dictionary learning and sparse representation based on subspace projections

2015 23nd Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu.2015.7130157 ◽

2015 ◽

Author(s):

Mahmoud Nazzal ◽

Huseyin Ozkaramanli

Keyword(s):

Sparse Representation ◽

Dictionary Learning ◽

Subspace Projections ◽

Structured Dictionary

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Image mixed gaussian and impulse noise elimination based on sparse representation model

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v23.i3.pp1440-1450 ◽

2021 ◽

Vol 23 (3) ◽

pp. 1440

Author(s):

Ahmed Abdulqader Hussein ◽

Sabahaldin A. Hussain ◽

Ahmed Hameed Reja

Keyword(s):

Sparse Representation ◽

Image Denoising ◽

Impulse Noise ◽

Heavy Tails ◽

Noise Distribution ◽

Self Similarity ◽

Noise Elimination ◽

Variational Framework ◽

Mixed Noise ◽

State Of Art

<p>A modified mixed Gaussian plus impulse image denoising algorithm based on weighted encoding with image sparsity and nonlocal self-similarity priors regularization is proposed in this paper. The encoding weights and the priors imposed on the images are incorporated into a variational framework to treat more complex mixed noise distribution. Such noise is characterized by heavy tails caused by impulse noise which needs to be eliminated through proper weighting of encoding residual. The outliers caused by the impulse noise has a significant effect on the encoding weights. Hence a more accurate residual encoding error initialization plays the important role in overall denoising performance, especially at high impulse noise rates. In this paper, outliers free initialization image, and an easier to implement a parameter-free procedure for updating encoding weights have been proposed. Experimental results demonstrate the capability of the proposed strategy to recover images highly corrupted by mixed Gaussian plus impulse noise as compared with the state of art denoising algorithm. The achieved results motivate us to implement the proposed algorithm in practice.</p>

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Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

Scientific Programming ◽

10.1155/2021/7008406 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Quan Yuan ◽

Zhenyun Peng ◽

Zhencheng Chen ◽

Yanke Guo ◽

Bin Yang ◽

...

Keyword(s):

Image Denoising ◽

Gaussian Noise ◽

Medical Image ◽

Impulse Noise ◽

Medical Images ◽

Noise Removal ◽

White Gaussian Noise ◽

Low Rank ◽

Rank Constraint ◽

Nonlocal Regularization

Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.

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