Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (5): 173-182.DOI: 10.3778/j.issn.1002-8331.1911-0313

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Super-Resolution Image Reconstruction Algorithm Using Sparse Features in Subspace

SHEN Yu, LIU Cheng, YANG Qian   

  1. 1.School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
    2.Gansu Artificial Intelligence and Image Processing Engineering Research Center,Lanzhou 730070, China
  • Online:2021-03-01 Published:2021-03-02



  1. 1.兰州交通大学 电子与信息工程学院,兰州 730070
    2.甘肃省人工智能与图形图像处理工程研究中心,兰州 730070


In the traditional super-resolution image reconstruction algorithm, the gradient of the image, the texture structure and other features are usually extracted by artificially designed rules. For complex and content-rich images, the extracted features cannot accurately represent all the information of the image, the edge of the image and local details will be missing. Moreover, in the image training process, there are also problems that the the number offeature maps of LR and HR image is inconsistent, and the feature matching degree is low. Therefore, how to extract the more expressive features as the accurate representation of the source image and improve the image feature matching during the training process is essential for the super-resolution reconstruction of the image. Aiming at the above problems, a super-resolution image reconstruction algorithm based on PCANet model is proposed. Firstly, the deep feature of the image is extracted by the PCANet model with Gaussian kernel function, and the sparse optimization algorithm is added to iteratively optimize the output feature map matrix to obtain the optimal projection matrix, which effectively improves the robustness of the feature map. Then, the deep learning feature of the extracted image is decomposed into multiple sparse features by using the learned LR filter. After the optimal solution is obtained by using the ADMM algorithm and the SA-ADMM algorithm to iteratively update, the sparse feature and mapping function of the LR image are combined. The sparse feature representation of the HR image is estimated, and finally convolved and summed with the corresponding HR filter to obtain the final reconstructed image. The experimental results show that this method can better retain the detailed information of the reconstructed image, the edge texture of the image is clearer, and the average value of the objective evaluation index has increased by more than 0.21?dB, which effectively improves the quality of image reconstruction.

Key words: PCANet model, sparse optimization, SA-ADMM algorithm, sparse feature, super-resolution image reconstruction



关键词: PCANet模型, 稀疏优化, SA-ADMM算法, 稀疏特征, 超分辨率图像重建