Lightweight Stereo Image Super-Resolution Using modified Parallax Attention

Recent smartphones employ multi-camera setups for capturing images, prompting the exploration of stereo image super-resolution (SSR) algorithms. SSR uses the complementary information provided by a binocular system to upscale input stereo image pairs. The effectiveness of SSR algorithms depends on successfully utilizing the stereo information from the training images. This paper, proposes a lightweight stereo image super-resolution method using modified parallax attention (LmPASSR), which enhances the utilization of stereo information. This is achieved through a modified occlusion mask that filters out irrelevant attention values. Additionally, the model incorporates depth-wise convolutions, implemented as D-blocks, to minimize parameter usage. Experimental results demonstrate that despite having fewer parameters, the proposed model produces results comparable to state-of-the-art (SOTA) methods.