Color Image Super-Resolution Reconstruction Based on Sparse Representation

Min Fen Shen; Long Shan Zhang; Huai Zheng Fu

doi:10.4028/www.scientific.net/AMM.278-280.1221

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 278-280Color Image Super-Resolution Reconstruction Based...

Color Image Super-Resolution Reconstruction Based on Sparse Representation

Abstract:

This paper proposes a YUV color image super-resolution reconstruction algorithm based on sparse representation. The R, G, B components of color image are highly correlated, three-channel super-resolution independent reconstruction will lead to color distortion, so in this paper the color image is firstly converted to the Y, U, V three channels, and then super-resolution reconstruction. For choosing the regularization parameter, this paper proposes an adaptive regularization parameter method; it has a good inhibitory effect on image noise and adaptive super-resolution reconstruction of color images. The results of experiment show that the proposed algorithm has a better PSNR, compared with bicubic interpolation method and sparse representation. The adaptive super-resolution reconstruction can further improve the quality of the reconstructed image and the method is robust to image noise.

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Periodical:

Applied Mechanics and Materials (Volumes 278-280)

Pages:

1221-1227

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.278-280.1221

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Online since:

January 2013

Authors:

Min Fen Shen, Long Shan Zhang, Huai Zheng Fu

Keywords:

Adaptive Reconstruction, Color Images, Sparse Representation, Super-Resolution Reconstruction

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