Computer Science ›› 2020, Vol. 47 ›› Issue (6): 138-143.doi: 10.11896/jsjkx.190500047

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Color Image Super-resolution Algorithm Based on Inter-channel Correlation and Nonlocal Self-similarity

MO Cai-wang1, CHANG Kan1,2,3, LI Heng-xin1, LI Ming-hong1, QIN Tuan-fa1,2,3   

  1. 1 School of Computer and Electronic Information,Guangxi University,Nanning 530004,China
    2 Guangxi Key Laboratory of Multimedia Communications and Network Technology,Guangxi University,Nanning 530004,China
    3 Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing,Guangxi University, Nanning 530004,China
  • Received:2019-05-09 Online:2020-06-15 Published:2020-06-10
  • About author:MO Cai-wang,born in 1993,postgra-duate.Her main research interests include image super-resolution and denoising.
    CHANG KAN,born in 1983,Ph.D,associate professor,master supervisor,is a member of China Computer Federation.His main research interests include image and video processing,compressive sensing,video coding,etc.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61761005,61761007) and Natural Science Foundation of Guangxi Zhuang Autonomous Region(2016GXNSFAA380154).

Abstract: Most of the existing single image super-resolution (SR) algorithms are designed to improve the resolution of a single channel.When dealing with the color images,the inter-channel correlation is ignored,so the reconstructed high resolution (HR) image is prone to distortion.To solve the above problem,this paper proposes a SR algorithm for color images,which jointly takes the inter-channel correlation (ICC) and non-local self-similarity (NLSS) into consideration.First of all,in order to fully make use of the inter-channel correlation of color images,the total variation (TV)-norm of the residual signals and the TV-norm of the ave-rage signal of three color channels are respectively calculated.Secondly,to further improve the SR results,the reconstructed HR images are updated based on the non-local self-similarity of nature images.Finally,to solve the established optimization problem,a split-Bregman method-based iteration is proposed.The proposed algorithm is compared with several state-of-the-art methods.At a scale factor of 3,the average peak signal to noise ratio (PSNR) improvement achieved by the proposed algorithm reaches 0.5 dB on Set5,and 0.36 dB on Set14,respectively.The experimental results demonstrate that jointly utilizing ICC and NLSS is able to effectively improve the quality of the reconstructed HR color images.

Key words: Color image super-resolution, Inter-channel correlation, Non-local self-similarity, Split-Bregman, Total variation

CLC Number: 

  • TP37
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