Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (6): 1777-1783.DOI: 10.11772/j.issn.1001-9081.2017112742

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Image super-resolution reconstruction based on four-channel convolutional sparse coding

CHEN Chen, ZHAO Jianwei, CAO Feilong   

  1. Department of Information and Mathematics, China Jiliang University, Hangzhou Zhejiang 310018, China
  • Received:2017-11-19 Revised:2017-12-29 Online:2018-06-10 Published:2018-06-13
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61571410, 61672477), the Natural Science Foundation of Zhejiang Province (LY18F020018).


陈晨, 赵建伟, 曹飞龙   

  1. 中国计量大学 信息与数学系, 杭州 310018
  • 通讯作者: 赵建伟
  • 作者简介:陈晨(1992-),男,浙江杭州人,硕士研究生,主要研究方向:智能计算、图像处理;赵建伟(1977-),女,浙江金华人,教授,博士,CCF会员,主要研究方向:智能计算、图像处理;曹飞龙(1965-),男,浙江台州人,教授,博士,CCF会员,主要研究方向:智能计算、图像处理。
  • 基金资助:

Abstract: In order to solve the problem of low resolution of iamge, a new image super-resolution reconstruction method based on four-channel convolutional sparse coding was proposed. Firstly, the input image was turned over 90° in turn as the input of four channels, and an input image was decomposed into the high frequency part and the low frequency part by low pass filter and gradient operator. Then, the high frequency part and low frequency part of the low resolution image in each channel were reconstructed by convolutional sparse coding method and cubic interpolation method respectively. Finally, the four-channel output images were weighted for mean to obtain the reconstructed high resolution image. The experimental results show that the proposed method has better reconstruction effect than some classical super-resolution methods in Peak Signal-to-Noise Ratio (PSNR), Structural SIMilarity (SSIM) and noise immunity. The proposed method can not only overcome the shortcoming of consistency between image patches destroyed by overlapping patches, but also improve the detail contour of reconstructed image, and enhance the stability of reconstructed image.

Key words: image reconstruction, super-resolution, convolutional sparse coding, four-channel, stability

摘要: 针对图像分辨率较低的问题,提出了一种基于四通道卷积稀疏编码的图像超分辨率重建方法。首先,该方法将输入图像依次翻转90°作为四通道的各自输入,通过低通滤波和梯度算子将输入图像分解成高频和低频部分;接着,分别利用卷积稀疏编码方法和三次插值方法对各通道低分辨率图像的高频部分和低频部分进行重建;最后,对四通道输出图像加权求均值获得重建的高分辨率图像。实验结果表明,所提方法比一些经典的超分辨率重建方法在峰值信噪比(PSNR)、结构相似度(SSIM)和抗噪性上具有更好的重建效果。所提方法不仅克服了重叠补丁破环图像补丁间一致性的缺陷,还提高了重建图像的细节轮廓,加强了重建图像的稳定性。

关键词: 图像重建, 超分辨率, 卷积稀疏编码, 四通道, 稳定性

CLC Number: