23 September 2014 MODIS images super-resolution algorithm via sparse representation
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Abstract
Based on the current mainstream algorithms, an effective super-resolution algorithm via sparse representation for MODIS remote sensing images is proposed in the paper. The basic idea behind the proposed algorithm is to obtain the redundant dictionaries deriving from high-resolution Landsat ETM+ images and low-resolution MODIS images, further give the instruction for reconstructing high-resolution MODIS images. Feature extraction is one vital part included in the procedure of dictionary training. The features are extracted from the wavelet-domain images as training samples, and then more effective dictionaries for high-resolution image reconstruction are obtained by applying the k-singular value decomposition (K-SVD) dictionary training algorithm. The experimental results demonstrate the proposed algorithm improved the reconstruction quality both visually and quantitatively. Compared with the traditional algorithm, the PSNR value approximately increases by 1.1 dB and SSIM value increases by 0.07. Moreover, both the quality and computational efficiency of the proposed algorithm can be improved given the appropriate number of atoms.
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Yue Pang, Yue Pang, Lingjia Gu, Lingjia Gu, Ruizhi Ren, Ruizhi Ren, Jian Sun, Jian Sun, } "MODIS images super-resolution algorithm via sparse representation", Proc. SPIE 9217, Applications of Digital Image Processing XXXVII, 921729 (23 September 2014); doi: 10.1117/12.2060310; https://doi.org/10.1117/12.2060310
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