Lightweight Image Super-Resolution Reconstruction Guided by Gated Feedback and LatticeFormer
Year: 2025
Type: article
Abstract: Despite the excellent performance of Transformers in image super-resolution reconstruction, their high complexity and single forward-feed information flow limit the effective mining of deep information. To address this issue, we propose a lightweight image super-resolution reconstruction model guided by gated feedback and LatticeFormer. Firstly, the reconstruction effect is significantly improved without adding extra parameters by proposing a gated feedback-LatticeFormer block as the basic block in the feedback block. Then, a gating block is designed to integrate deep global information from the previous iteration to enhance the expression of shallow features. Finally, continuous LatticeFormer blocks are constructed to improve the network’s feature extraction capability further using an attention mechanism, thus achieving effective acquisition of global features at different levels. Compared with other existing lightweight image super-resolution reconstruction methods, our method on datasets such as Set5, Set14, BSD100, Urban100, and Manga109 for$2 \times $,$3 \times $, and$4 \times $tasks reduces the model parameters by 3%, 5%, and 7% respectively, while increasing the Peak Signal-to-Noise Ratio (PSNR) by 0.18-1.05 dB, 0.17-0.82 dB, and 0.18-1.00 dB respectively. Additionally, in the$2 \times $and$4 \times $tasks of the Thangka dataset, the PSNR is increased by 0.89 dB and 0.60 dB respectively. Experimental results show that our method can generate high-quality reconstructed images with complete structures and clear texture details.
Source: IEEE Access
Authors: Yao Fan, Min Jiang, Zhaoyang Liu, Zhiwei Zhao, Lizhi Zheng
Institution: Xizang Minzu University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4408791925