Study on multi-spectral remote sensing image restoration based on sparse representation

The multispectral remote sensing image sensor is expensive, and it will cause part of the pixels of the defect in the process of acquisition and transmission, it would be of great importance to be able to restore the defective pixels at the receiver. According to the latest research results of optimization, this paper presents a new multi-spectral remote sensing image restoration method based on sparse representation. The method can divide three-dimensional image into different blocks and model the problem of multi-spectral remote sensing image, and the multi-spectral pixel blocks of the study area is restored by sparse approximation. The experiment proves the efficiency of the algorithm, and the proposed method is very important in remote sensing image processing.

[1]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[2]  Jon Atli Benediktsson,et al.  Recent Advances in Techniques for Hyperspectral Image Processing , 2009 .

[3]  Lei Zhang,et al.  Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Gui Zhou Wang,et al.  A Modified Multi Scale Retinex with Color Restoration Algorithm for Automatic Enhancement of Landsat-5 Remote Sensing Image , 2011 .

[5]  Abbas Najafipour,et al.  Comparing the trustworthiness of signal-to-noise ratio and peak signal-to-noise ratio in processing noisy partial discharge signals , 2013 .

[6]  R. Keith Robinson,et al.  Hyperspectral Imaging on the International Space Station: An Innovative Approach to Commercial Development of Space , 2004 .

[7]  Yong Zhao,et al.  Optimizing the slow light properties of slotted photonic crystal waveguide and its application in a high-sensitivity gas sensing system , 2013 .

[8]  Mingyi He,et al.  Remote sensing image restoration based on compressive sensing and two-step iteration shrinkage algorithm , 2010, Optical Engineering + Applications.

[9]  Yuanjie Zheng,et al.  Groupwise registration of sequential images from multispectral imaging (MSI) of the retina and choroid. , 2016, Optics express.

[10]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[11]  Xuewen Wu,et al.  A New Automatic Target Recognition Scheme Based on Model Simulation and Structured Learning , 2014 .

[12]  Michael Elad,et al.  Learning Multiscale Sparse Representations for Image and Video Restoration , 2007, Multiscale Model. Simul..

[13]  G. Shaw,et al.  Signal processing for hyperspectral image exploitation , 2002, IEEE Signal Process. Mag..

[14]  Djemel Ziou,et al.  Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.

[15]  José M. Bioucas-Dias,et al.  Fast Image Recovery Using Variable Splitting and Constrained Optimization , 2009, IEEE Transactions on Image Processing.

[16]  Jon Atli Benediktsson,et al.  Introduction to the Issue on Advances in Remote Sensing Image Processing , 2011, IEEE J. Sel. Top. Signal Process..

[17]  Chein-I Chang,et al.  Estimation of number of spectrally distinct signal sources in hyperspectral imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Marc Teboulle,et al.  Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems , 2009, IEEE Transactions on Image Processing.

[19]  nasa.  Hyperspectral Imaging on the International Space Station: An Innovative Approach to Commercial Development of Space , 2019 .

[20]  Xiaokang Yang,et al.  Sub clustering K-SVD: Size variable dictionary learning for sparse representations , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).