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Remote Sensing Image Super-resolution Reconstructionin Multi-scale Compressed Sensing Framework(PDF)


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Remote Sensing Image Super-resolution Reconstructionin Multi-scale Compressed Sensing Framework
Chen WeiyeSun Quansen
School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
remote sensing imagesuper-resolution reconstructionmulti-scalecompressed sensing
The traditional compressed sensing based super-resolution reconstruction algorithm regards images as a single scale without considering that different scale image patches may have different discriminant information. To effectively utilize the scale characteristics of remote sensing images,a new remote sensing image super-resolution reconstruction algorithm in the multi-scale compressed sensing framework was proposed. First,image patches were clustered to construct multi-scale training sample sets. Next,the Fisher criterion was used to learn a discriminative dictionary containing the classification information of remote sensing images. Then,the acquisition process of the low-resolution image was estimated by the construction method of the measurement matrix in compressed sensing. Finally,the sub-region images in the multi-scale mode were reconstructed by using the discriminant dictionary. The experimental results demonstrate that it is effective to introduce multi-scale compressed sensing into image super-resolution reconstruction. The proposed algorithm outperforms other existing algorithms both in visual qualities and evaluation criteria.


[1] DONOHO D L. Compressed sensing[J]. IEEE transactions on information theory,2006,52(4):1 289-1 306.
[2]TSAIG Y,DONOHO D L. Extensions of compressed sensing[J]. Signal processing,2006,86(3):549-571.
[3]CANDèS E J,WAKIN M B. An introduction to compressive sampling[J]. IEEE signal processing magazine,2008,25(2):21-30.
[4]FAN N. Super-resolution using regularized orthogonal matching pursuit based on compressed sensing theory in the wavelet domain[C]//IEEE international conference on computer graphics,imaging and visualization. Tianjin,China:IEEE,2009:349-354.
[5]YANG J,WRIGHT J,HUANG T S,et al. Image super-resolution via sparse representation[J]. IEEE transactions on image processing,2010,19(11):2 861-2 873.
[6]YANG S,SUN F,WANG M,et al. Novel super resolution restoration of remote sensing images based on compressive sensing and example patches-aided dictionary learning[C]//IEEE international workshop on multi-platform/multi-sensor remote sensing and mapping. Xiamen,China:IEEE,2011:1-6.
[7]KULKARNI N,NAGESH P,GOWDA R,et al. Understanding compressive sensing and sparse representation-based super-resolution[J]. IEEE transactions on circuits and systems for video technology,2012,22(5):778-789.
[8]PAN Z,YU J,HUANG H,et al. Super-resolution based on compressive sensing and structural self-similarity for remote sensing images[J]. IEEE transactions on geoscience and remote sensing,2013,51(9):4 864-4 876.
[9]DONG W,ZHANG L,SHI G,et al. Nonlocally centralized sparse representation for image restoration[J]. IEEE transactions on image processing,2013,22(4):1 620-1 630.
[10]TRINH D,LUONG M,DIBOS F,et al. Novel example-based method for super-resolution and denoising of medical images[J]. IEEE transactions on image processing,2014,23(4):1 882-1 895.
[11]REN K,XU F. Super-resolution images fusion via compressed sensing and low-rank matrix decomposition[J]. Infrared physics and technology,2015,68:61-68.
[12]DONG C,LOY C C,HE K,et al. Image super-resolution using deep convolutional networks[J]. IEEE transactions on pattern analysis and machine intelligence,2016,38(2):295-307.
[13]DAUBECHIES I,DEFRISE M,DE MOL C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J]. Communications on pure and applied mathematics,2004,57(11):1 413-1 457.
[14]ZHANG X,BURGER M,BRESSON X,et al. Bregmanized nonlocal regularization for deconvolution and sparse reconstruction[J]. SIAM journal on imaging sciences,2010,3(3):253-276.
[15]ZHANG Z,RAO B D. Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation[J]. IEEE transactions on signal processing,2013,61(8):2 009-2 015.
[16]BRADY D J,HAGEN N. Multiscale lens design[J]. Optics express,2009,17(13):10 659-10 674.
[17]YANG M,ZHANG L,FENG X,et al. Fisher discrimination dictionary learning for sparse representation[C]//IEEE international conference on computer vision. Barcelona,Spain:IEEE,2011:543-550.
[18]GAN L. Block compressed sensing of natural images[C]//IEEE international conference on digital signal processing. Cardiff,UK:IEEE,2007:403-406.


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