StereoMamba: Enhancing Stereo Image Super-Resolution with Structured State Space Models and Bi-Directional Cross Attention

Abstract

Stereo image super-resolution (SR) aims to enhance image resolution by leveraging the complementary information from stereo image pairs. While convolutional neural network (CNN)-based methods have traditionally dominated this field, they struggle with capturing long-range dependencies. Transformer-based approaches have shown improvements by better modeling long-range dependencies, but their computational complexity scales quadratically with respect to the window length. To address these challenges, in this paper we propose StereoMamba, a new stereo image super-resolution method built on Structured State Space Models (SSMs). StereoMamba leverages the Mamba architecture to effectively capture long-range dependencies and inter-view correlations in stereo image pairs. Additionally, we introduce a Stereo Bi-directional Cross-Attention Module (SBCAM) to further improve stereo view correlation. Extensive experiments show that StereoMamba consistently surpasses state-of-the-art methods across several public datasets.