Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution

Yunxiang Li, Wenbin Zou, Qiaomu Wei, Feng Huang, Jing Wu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6489-6498

Stereo image super-resolution utilizes the cross-view complementary information brought by the disparity effect of left and right perspective images to reconstruct higher-quality images. Cascading feature extraction modules and cross-view feature interaction modules to make use of the information from stereo images is the focus of numerous methods. However this adds a great deal of network parameters and structural redundancy. To facilitate the application of stereo image super-resolution in downstream tasks we propose an efficient Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution (MFFSSR). Specifically MFFSSR utilizes the Hybrid Attention Feature Extraction Block (HAFEB) to extract multi-level intra-view features. Using the channel separation strategy HAFEB can efficiently interact with the embedded cross-view interaction module. This structural configuration can efficiently mine features inside the view while improving the efficiency of cross-view information sharing. Hence reconstruct image details and textures more accurately. Abundant experiments demonstrate the effectiveness of MFFSSR. We achieve superior performance with fewer parameters. The source code is available at https://github.com/KarosLYX/MFFSSR.