Image super-resolution reconstruction based on sparse representation and deep learning

作者：

Highlights：

• The innovation of this thesis lies in the combination of sparse coding and deep learning.

• We use the Gabor transform and NSCT transform to propose the MFSCSR algorithm, which is better than the SCSR algorithm.

• We improve the VDSR deep network and introduce the feature fusion idea, and propose the multi-residual network.

• We combine the MFSCSR algorithm with the MR network to obtain the MRMFSCSR algorithm, which has better image reconstruction effect than the VDSR algorithm.

摘要

•The innovation of this thesis lies in the combination of sparse coding and deep learning.•We use the Gabor transform and NSCT transform to propose the MFSCSR algorithm, which is better than the SCSR algorithm.•We improve the VDSR deep network and introduce the feature fusion idea, and propose the multi-residual network.•We combine the MFSCSR algorithm with the MR network to obtain the MRMFSCSR algorithm, which has better image reconstruction effect than the VDSR algorithm.

论文关键词：Sparse representation,Deep learning,Super-resolution,Feature fusion

论文评审过程：Received 15 May 2019, Revised 9 February 2020, Accepted 16 June 2020, Available online 23 June 2020, Version of Record 29 June 2020.

论文官网地址：https://doi.org/10.1016/j.image.2020.115925