DEDD: Stereo Image Super-Resolution Reconstruction Based on Disparity Estimation and Domain Diffusion

Image super-resolution (SR) and disparity estimation are closely related in stereo images, and exploiting the features within disparity maps effectively enhances the performance of SR. In this paper, we proposed a stereo image super-resolution reconstruction network based on disparity estimation and domain diffusion model. The network combines disparity estimation and diffusion models to perform feature summation and construct a domain space, facilitating interactionss between forward and backward diffusion memory units. To further enhance the quality of the reconstructed images, we introduce a different memory unit using the combinatorial concept of long short-term memory. Furthermore, disparity loss and structural similarity are incorporated into the loss function, aiming to yield more accurate results. Experiments demonstrate that our method outperforms existing approaches and achieves a 23% reduction in the number of parameters. Finally, the proposed method is applied to underwater stereo images. The results showcase excellent prospects for application.