  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.
