A block-thresholding method for multispectral image denoising

2005 ◽  
Author(s):  
Caroline Chaux ◽  
Amel Benazza-Benyahia ◽  
Jean-Christophe Pesquet
Keyword(s):  
Image Denoising ◽  
Multispectral Image ◽  
Thresholding Method ◽  
Block Thresholding

scholarly journals An Adapted Block Thresholding Method for Omnidirectional Image Denoising

2014 ◽  
Vol 8 (18) ◽  
pp. 1966-1972
Author(s):  
Brahim Alibouch ◽  
Abderrazak Iazzi ◽  
Amina Radgui ◽  
Mohammed Rziza
Keyword(s):  
Image Denoising ◽  
Thresholding Method ◽  
Omnidirectional Image ◽  
Block Thresholding

scholarly journals Multispectral image denoising using sparse and graph Laplacian Tucker decomposition

2020 ◽  
Vol 6 (3) ◽  
pp. 319-331
Author(s):  
Xiaoce Wu ◽  
Bingyin Zhou ◽  
Qingyun Ren ◽  
Wei Guo
Keyword(s):  
Prior Knowledge ◽  
Image Denoising ◽  
Graph Laplacian ◽  
Noise Removal ◽  
Multispectral Image ◽  
Low Rank ◽  
Tucker Decomposition ◽  
Spectral Bands ◽  
Global Correlation ◽  
Ill Posed

Abstract Multispectral image denoising is a basic problem whose results affect subsequent processes such as target detection and classification. Numerous approaches have been proposed, but there are still many challenges, particularly in using prior knowledge of multispectral images, which is crucial for solving the ill-posed problem of noise removal. This paper considers both non-local self-similarity in space and global correlation in spectrum. We propose a novel low-rank Tucker decomposition model for removing the noise, in which sparse and graph Laplacian regularization terms are employed to encode this prior knowledge. It can jointly learn a sparse and low-rank representation while preserving the local geometrical structure between spectral bands, so as to better capture simultaneously the correlation in spatial and spectral directions. We adopt the alternating direction method of multipliers to solve the resulting problem. Experiments demonstrate that the proposed method outperforms the state-of-the-art, such as cube-based and tensor-based methods, both quantitatively and qualitatively.

Improved Wavelet Thresholding for CCD Measurement Image Denoising

2014 ◽  
Vol 574 ◽  
pp. 432-435 ◽  
Author(s):  
Jie Zhan ◽  
Zhen Xing Li
Keyword(s):  
Image Denoising ◽  
Threshold Function ◽  
Wavelet Thresholding ◽  
Wavelet Coefficients ◽  
Local Correlation ◽  
Thresholding Method ◽  
Threshold Functions ◽  
Soft Threshold ◽  
Simulation Results ◽  
Filtering Effect

An improved wavelet thresholding method is presented and successfully applied to CCD measuring image denoising. On the analysis of the current widely used soft threshold and hard threshold, combining characteristics of the CCD measuring image and use of local correlation of wavelet coefficients, an improved threshold function is proposed, and the denoising results were contrasted among different threshold functions. The simulation results show that adopting the improved threshold function can acquire better filtering effect than traditional soft threshold and hard threshold methods.

scholarly journals Minimum noiseless description length (MNDL) thresholding

2021 ◽  
Author(s):  
Azadeh Fakhrzadeh
Keyword(s):  
Image Denoising ◽  
Additive Noise ◽  
Random Variable ◽  
Data Driven ◽  
Mean Square ◽  
Chi Square ◽  
Denoising Method ◽  
Thresholding Method ◽  
Simulation Results ◽  
Subspace Selection

In this thesis, the problem of data denoising is considered and a new data denoising method is developed. This approach is an adaptive, data-driven thresholding method that is based on Minimum Noiseless Description Length (MNDL). MNDL is an approach to subspace selection which estimates bounds on the desired Mean Square Error (MSE). The subspace minimizing these bounds is chosen as the optimum one. In this research, we explore application of MNDL Subspace Selection (MNDL-SS) as a thresholding method. Although the basic idea and desired criterion of MNDL thresholding and MNDL-SS are the same, the challenges in calculation of the desired criterion in MNDL thresholding are very different. In MNDL-SS, the additive noise effects are in the form of samples of a Chi-Square random variable. However, this assumption does not hold for MNDL thresholding anymore. In this research, we developed a new method for calculation of the desired criterion based on characteristics of noise in thresholding. Our simulation results show that MNDL thresholding outperforms the compared methods. In this thesis, we also explore the area of image denoising. In image denoising approaches, some properties of the image are considered. One of the well known image denoising methods, that outperforms other methods, is BayesShrink. We compare our method with BayesShrink. We show that the results of MNDS thresholding are comparable with BayesShrink in our simulations.

scholarly journals An algorithm for wavelet thresholding based image denoising by representing images in hexagonal lattice

2018 ◽  
Vol 16 (2) ◽  
Author(s):  
Jeevan K. M ◽  
S. Krishnakumar
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Hexagonal Lattice ◽  
Window Size ◽  
Wavelet Thresholding ◽  
Rectangular Grid ◽  
Hexagonal Grid ◽  
Thresholding Method ◽  
Unbiased Risk ◽  
Grid Processing

The existing method of representation for digital images is using square shaped picture elements called pixels in a rectangular grid. Processing based on hexagonal grid is a new approach in image processing. It has various advantages like symmetry, higher angular resolution, consistent connectivity and higher sampling efficiency. Image processing applications like rotation, scaling, edge detection, and compression in hexagonal domain have already been discussed by many researchers. In this paper we propose an image denoising scheme in hexagonal lattice using wavelet thresholding method. For the thresholding of wavelet coefficients, modified NeighShrink thresholding method is applied. In NeighShrink method, sub-optimal universal threshold and identical neighboring window size in all wavelet sub-bands are used. However, in the proposed method, instead of sub-optimal universal threshold, an optimal threshold is determined for every wavelet sub-band by the Stein’s Unbiased Risk Estimate (SURE). Denoising is performed on images represented in rectangular grid as well as hexagonal grid using modified thresholding method for comparison. MSE, PSNR and SSIM are used for the performance analysis. The obtained results confirm that the proposed method gives better results than existing algorithms.

A method for remote sensing image denoising based on structured dictionary learning and cloud computing

2020 ◽  
pp. 002072092093107
Author(s):  
Ru Yang ◽  
Zhentao Qin ◽  
Xiangyu Zhao
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Image Denoising ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Multispectral Image ◽  
Structured Dictionary

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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