  Under stereo settings, the problem of image super-resolution (SR) and
disparity estimation are interrelated that the result of each problem could
help to solve the other. The effective exploitation of correspondence between
different views facilitates the SR performance, while the high-resolution (HR)
features with richer details benefit the correspondence estimation. According
to this motivation, we propose a Stereo Super-Resolution and Disparity
Estimation Feedback Network (SSRDE-FNet), which simultaneously handles the
stereo image super-resolution and disparity estimation in a unified framework
and interact them with each other to further improve their performance.
Specifically, the SSRDE-FNet is composed of two dual recursive sub-networks for
left and right views. Besides the cross-view information exploitation in the
low-resolution (LR) space, HR representations produced by the SR process are
utilized to perform HR disparity estimation with higher accuracy, through which
the HR features can be aggregated to generate a finer SR result. Afterward, the
proposed HR Disparity Information Feedback (HRDIF) mechanism delivers
information carried by HR disparity back to previous layers to further refine
the SR image reconstruction. Extensive experiments demonstrate the
effectiveness and advancement of SSRDE-FNet.
