Super-resolution image reconstruction based on convolutional sparse coding and generative adversarial networks

DU Jun-sen; GUO Jie-long; YU Hui; WEI Xian

doi:10.37188/CJLCD.2022-0406

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Super-resolution image reconstruction based on convolutional sparse coding and generative adversarial networks

Image Processing | 更新时间：2023-10-10

- Super-resolution image reconstruction based on convolutional sparse coding and generative adversarial networks
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 10, Pages: 1423-1433(2023)
- 作者机构：
  
  1.福州大学先进制造学院，福建泉州 362000
  2.中国科学院福建物质结构研究所，福建福州 350108
  3.中国科学院海西研究院泉州装备制造研究中心，福建泉州 362000
- 作者简介：
- 基金信息：
  
  Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China(2021ZZ120);Fujian Science and Technology Plan(2021T3003);Quanzhou Science and Technology Plan(2021C065L)
- DOI：10.37188/CJLCD.2022-0406
  CLC：
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DU Jun-sen, GUO Jie-long, YU Hui, et al. Super-resolution image reconstruction based on convolutional sparse coding and generative adversarial networks. [J]. Chinese Journal of Liquid Crystals and Displays 38(10):1423-1433(2023)
DOI：

DU Jun-sen, GUO Jie-long, YU Hui, et al. Super-resolution image reconstruction based on convolutional sparse coding and generative adversarial networks. [J]. Chinese Journal of Liquid Crystals and Displays 38(10):1423-1433(2023) DOI： 10.37188/CJLCD.2022-0406.

摘要

针对现有图像超分辨率重建算法的重建图像仍存在高频信息缺失、噪点增多问题，本文提出了一种基于卷积稀疏编码与生成对抗网络的图像超分辨率重建模型。首先，利用卷积网络实现稀疏编码并获取图像稀疏表示，充分利用图像的先验信息，有效避免重建图像高频信息缺失和噪点增多的问题；在得到低分辨率图像的稀疏表示后，通过重建模块对稀疏表示进行重建得到超分辨率图像；随后，鉴别器对重建图像进行鉴别，缓解由PSNR主导的算法导致重建图像趋于平滑的问题。在不断对抗训练后，最后的重建图像具有更好的视觉效果。本文在Set5、Set14、BSD100和Urban100通用测试数据集上进行2倍和4倍的超分辨率重建实验，并与Bicubic、SRGAN、EDSR和ESRGAN对比。与ESRGAN方法相比，本文模型在4个数据集上平均PSNR提升约0.702 8 dB，平均SSIM提升约0.047，平均LPIPS提升了0.016。实验结果表明，所提出的模型具有较强的竞争力，能够恢复更多的细纹理细节且具有更好的清晰度。

Abstract

To address the problems of high-frequency information missing and increased noise in images generated by existing image super-resolution reconstruction algorithms， this paper proposes an image super-resolution reconstruction model based on convolutional sparse coding and generative adversarial networks. Firstly， convolutional networks are employed to implement sparse coding and obtain a sparse representation of the image， which makes full use of the prior information of the image and effectively avoids the problems of high-frequency information missing and increased noise in the reconstructed image. After obtaining the sparse representation of the low-resolution image， the sparse representation is reconstructed by the reconstruction module to obtain the super-resolution image. Subsequently， the discriminator discriminates the reconstructed image to alleviate the problem that the reconstructed image tends to be smooth due to the PSNR-dominated algorithm. After continuous adversarial training， the final generated super-resolution images are made to have better visual effects. The super-resolution reconstruction experiments are performed on Set5， Set14， BSD100 and Urban100 general test datasets at 2× and 4× and compared with Bicubic， SRGAN， EDSR and ESRGAN methods. Compared with ESRGAN， the average PSNR improvement is about 0.702 8 dB， the average SSIM improvement is about 0.047， and the average LPIPS improvement is 0.016 on the four datasets.Experimental results show that the proposed model is highly competitive and enables the recovery of more fine-texture details with better definition.

关键词

图像处理图像超分辨率稀疏表示生成对抗网络

Keywords

image processingimage super resolutionsparse representationgenerative adversarial network

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