Abstract

In this paper, we propose a lightweight dual-path convolutional neural network for image super-resolution (SR). We introduce shift convolution and propose a shift-channel attention (shift-ca) mechanism to build an effective network. Shift-ca produces an attentional map with a larger field of view, and its formulation is similar to channel attention and spatial attention. In addition, we propose the Local Shift-Channel Attention Feature Extraction (LCFE) module as the main part of the Dual Path Shift Attention Block (DPSAB). Using the dual-path structure allows us to reduce the network depth and retain more original features for the subsequent up-sampling compensation operation. In the final HR reconstruction module, we combine the nearest neighbor upsampling layer, convolutional layer, and activation layer to form the compensated nearest neighbor upsampling module (C-NUM) to improve the reconstruction quality with a small parameter cost. Our final model is the Dual Path Shift Attention Network (DPSAN), and it achieves similar performance to the lightweight network WMRN (36.38

Keywords

MSC

68U10

Cite this article

@article{cai-6779,
  author  = {Yu Yang and Pan Wang and Yajuan Wu},
  title   = {Effective Lightweight Dual-Path Shift Compensation Network for Image Super-Resolution},
  journal = {Computing and Informatics},
  volume  = {43},
  number  = {2},
  pages   = {393--413},
  year    = {2024},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2024_2_393}
}
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