  Stereo image super-resolution (stereoSR) aims to enhance the quality of
super-resolution results by incorporating complementary information from an
alternative view. Although current methods have shown significant advancements,
they typically operate on representations at full resolution to preserve
spatial details, facing challenges in accurately capturing contextual
information. Simultaneously, they utilize all feature similarities to
cross-fuse information from the two views, potentially disregarding the impact
of irrelevant information. To overcome this problem, we propose a mixed-scale
selective fusion network (MSSFNet) to preserve precise spatial details and
incorporate abundant contextual information, and adaptively select and fuse
most accurate features from two views to enhance the promotion of high-quality
stereoSR. Specifically, we develop a mixed-scale block (MSB) that obtains
contextually enriched feature representations across multiple spatial scales
while preserving precise spatial details. Furthermore, to dynamically retain
the most essential cross-view information, we design a selective fusion
attention module (SFAM) that searches and transfers the most accurate features
from another view. To learn an enriched set of local and non-local features, we
introduce a fast fourier convolution block (FFCB) to explicitly integrate
frequency domain knowledge. Extensive experiments show that MSSFNet achieves
significant improvements over state-of-the-art approaches on both quantitative
and qualitative evaluations.
