@inproceedings{
gao2024learning,
title={Learning Optimal  Combination Patterns for Lightweight  Stereo Image Super-Resolution},
author={Hu Gao and Jing Yang and Ying Zhang and Jingfan Yang and Bowen Ma and Depeng Dang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=MM4xkBF9fv}
}
Stereo image super-resolution (stereoSR) strives to improve the quality of super-resolution  by leveraging the auxiliary information provided by another perspective. Most approaches concentrate on  refining module design, and stacking massive network blocks to extract and integrate information. Although there have been advancements, the memory and computation costs are increasing as well. To tackle this issue, we propose a lattice structure that autonomously learns the optimal combination patterns of network blocks, which enables the efficient and precise acquisition of feature representations, and ultimately achieves lightweight stereoSR. Specifically, we draw inspiration from the lattice phase equalizer and design lattice stereo NAFBlock (LSNB) to bridge pairs of NAFBlocks using re-weight block (RWBlock) through a coupled butterfly-style topological structures.  RWBlock empowers LSNB with the capability to explore various combination patterns of pairwise NAFBlocks by adaptive re-weighting of feature. Moreover, we propose a lattice stereo attention module (LSAM) to search and transfer the most relevant features from another view. The resulting tightly interlinked architecture, named as LSSR, extensive experiments  demonstrate that our method performs competitively to the  state-of-the-art.
Learning Optimal  Combination Patterns for Lightweight  Stereo Image Super-Resolution
Hu Gao Jing Yang Ying Zhang Jingfan Yang Bowen Ma Depeng Dang