计算机工程 ›› 2019, Vol. 45 ›› Issue (10): 272-276,282.doi: 10.19678/j.issn.1000-3428.0052065

• 图形图像处理 • 上一篇    下一篇

基于色彩约束与非局部稀疏表示的彩色图像超分辨率重建

徐志刚, 马强, 朱红蕾, 张墨逸   

  1. 兰州理工大学 计算机与通信学院, 兰州 730050
  • 收稿日期:2018-07-10 修回日期:2018-09-28 出版日期:2019-10-15 发布日期:2018-10-19
  • 作者简介:徐志刚(1977-),男,副教授、博士,主研方向为图像超分辨率重建、深度学习理论与方法;马强,硕士研究生;朱红蕾,副教授、硕士;张墨逸,讲师、硕士。
  • 基金项目:
    国家自然科学基金(61761028);模式识别国家重点实验室开放课题基金(201700005)。

Color Image Super-resolution Reconstruction Based on Color Constraint and Nonlocal Sparse Representation

XU Zhigang, MA Qiang, ZHU Honglei, ZHANG Moyi   

  1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
  • Received:2018-07-10 Revised:2018-09-28 Online:2019-10-15 Published:2018-10-19

摘要: 基于稀疏表示模型的彩色图像超分辨率重建方法通常采用基于图像块的稀疏编码过程,易导致稀疏表示不稳定、重建彩色图像存在细节模糊和色彩伪影的问题。为此,提出一种非局部稀疏表示与色彩通道约束相结合的重建算法。将待重建的低分辨率彩色图像转换到YCbCr色彩空间,利用非局部稀疏模型对低分辨率彩色图像的亮度信息进行重建,再将重建图像转换回RGB色彩空间,应用色彩通道约束方法去除色彩伪影,从而在保证图像细节信息重建质量的同时提升其色彩伪影的去除能力。实验结果表明,与双三次插值算法、ScSR算法等相比,该算法重建图像的峰值信噪比和结构相似性较高。

关键词: 稀疏表示, 超分辨率, 彩色图像, 非局部自相似性, 色彩通道约束

Abstract: Color image super-resolution reconstruction method based on sparse representation model usually adopts sparse coding process based on image blocks,which easily leads to the instability of sparse representation,and the problems of detail blurring and color artifacts in the reconstruction of color images.Therefore,a reconstruction algorithm combining nonlocal sparse representation with color channel constraints is proposed.The low-resolution color images are converted into YCbCr color space,and the brightness information of low-resolution color image is reconstructed by nonlocal sparse model.Then the reconstructed image is converted back to RGB color space,and the color artifacts are removed by using color channel constraints,thus ensuring the quality of image detail information reconstruction,and improving the ability in removing color artifacts.Experimental results show that compared with Bicubic Interpolation(BI) algorithm,ScSR algorithm and so on,the reconstructed image of the proposed algorithm has higher Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measurement(SSIM).

Key words: sparse representation, super-resolution, color image, nonlocal self-similarity, color channel constraint

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