Frequency-Decomposed Interaction Network for Stereo Image Restoration.

Abstract

Stereo image restoration in adverse environments, such as low-light conditions, rain, and low resolution, requires effective exploitation of cross-view complementary information to recover degraded visual content. In monocular image restoration, frequency decomposition has proven effective, where high-frequency components aid in recovering fine textures and reducing blur, while low-frequency components facilitate noise suppression and illumination correction. However, existing stereo restoration methods have yet to explore cross-view interactions by frequency decomposition, which is a promising direction for enhancing restoration quality. To address this, we propose a frequency-aware framework comprising a Frequency Decomposition Module (FDM), Detail Interaction Module (DIM), Structural Interaction Module (SIM), and Adaptive Fusion Module (AFM). FDM employs learnable filters to decompose the image into high- and low-frequency components. DIM enhances the high-frequency branch by capturing local detail cues through deformable convolution. SIM processes the low-frequency branch by modeling global structural correlations via a cross-view row-wise attention mechanism. Finally, AFM adaptively fuses the complementary frequency-specific information to generate high-quality restored images. Extensive experiments demonstrate the efficacy and generalizability of our framework across three diverse stereo restoration tasks, where it achieves state-of-the-art performance in low-light enhancement, rain removal, alongside highly competitive results in super-resolution. Our code is available at https://github.com/C2022J/FDIN.