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.

本研究提出了一种混合尺度选择性融合网络（MSSFNet），用于提高立体图像超分辨率（stereoSR）结果的质量，包括保留精确的空间细节和丰富的上下文信息，并自适应地选择和融合来自两个视角的最准确的特征，以增强高质量的 stereoSR 效果。