Hong Wang
ABSTRACT
Deblurring high resolution remote sensing image is a very important problem in remote sensing research. In this paper, we propose a new deblurring algorithm for high-resolution remote sensing images (HSI) based on sparse representation. The purpose of this study is to apply compressed sensing measurement and reconstruction technology to realize the processing of remote sensing image, and discuss the Under what circumstances can CS achieve better results in remote sensing image processing. The algorithm uses fast gradient projection algorithm to achieve deblurring and retain the important ground information of the original image. Experiments on remote sensing images obtained by GF-5 show that the algorithm can filter the blurring of remote sensing images well and improve the peak-to-noise ratio (PSNR) of images. The algorithm has better performance than other sparse representation algorithms. This paper explores the application of dictionary learning theory and sparse decomposition in remote sensing image processing. By further extending the algorithm proposed in this paper and adding new constraints, remote sensing image restoration, target recognition, deblurring, fusion and so on can be carried out.
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Index Terms
- Study on Hyperspectral Remote Sensing Images of GF-5 De-Blurring Based on Sparse Representation
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