计算机科学 ›› 2020, Vol. 47 ›› Issue (6): 138-143.doi: 10.11896/jsjkx.190500047

• 计算机图形学&多媒体 • 上一篇    下一篇

基于通道间相关性和非局部自相似性的彩色图像超分辨率算法

莫彩网1, 常侃1,2,3, 李恒鑫1, 李明鸿1, 覃团发1,2,3   

  1. 1 广西大学计算机与电子信息学院 南宁530004
    2 广西大学广西多媒体通信与网络技术重点实验室 南宁530004
    3 广西大学广西高校多媒体通信与信息处理重点实验室 南宁530004
  • 收稿日期:2019-05-09 出版日期:2020-06-15 发布日期:2020-06-10
  • 通讯作者: 常侃(pandack0619@163.com)
  • 作者简介:mcw525@yeah.net
  • 基金资助:
    国家自然科学基金项目(61761005,61761007);广西自然科学基金项目(2016GXNSFAA380154)

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).

摘要: 现有的大多数单图像超分辨率方法仅用于提高单个通道的分辨率。在处理彩色图像时,由于忽略了通道间的相关性,重建的高分辨率图像容易产生失真。针对这些问题,提出了一种综合考虑通道间相关性及非局部自相似性的彩色图像超分辨算法。首先,为了充分利用彩色图像的通道间相关性,分别计算通道间残差信号和三通道平均信号的总变分范数;其次,为了进一步提升超分辨率的结果,基于图像内的非局部自相似性更新重建图像;最后,为了求解所建立的优化问题,提出了基于split-Bregman方法的快速迭代算法。将所提算法与一些主流算法进行了比较,在3倍上采样条件下,所提算法在Set5和Set14数据集上平均可获得的峰值信噪比(Peak Signal to Noise Ratio,PSNR)增益分别为0.5 dB及0.36 dB。实验结果证明了联合应用通道间相关性及非局部自相似性能有效提升彩色图像的超分辨重建质量。

关键词: split-Bregman, 彩色图像超分辨率, 非局部自相似性, 通道间相关性, 总变分

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

中图分类号: 

  • TP37
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