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.

翻译：在立体设置下,图像超分辨率(SR)和差异估计问题是相互关联的,每个问题的结果都有助于解决另一个问题。有效地利用不同观点之间的通信有助于斯洛伐克表现,而高分辨率(HR)特征和较丰富细节有助于对通信估计。根据这一动机,我们提议建立一个立体超分辨率和差异估计反馈网络(SSRDE-FNet),同时在一个统一的框架内处理立体图像超分辨率和差异估计,并彼此互动,以进一步改进其业绩。具体地说,SREDE-FNet由两个双循环的分网络组成,供左面和右面观点使用。除了在低分辨率(LR)空间进行交叉浏览信息开发外,还利用斯洛伐克共和国进程产生的人力资源表述,以更准确的方式进行人力资源差异估计,通过这些特征可以汇总,产生更精确的SR结果。随后,拟议的人力资源差异信息反馈机制将人力资源差异信息传送回原层,以进一步完善SRM-F的图像重建。