Stereo image super-resolution aims to improve the quality of high-resolution
stereo image pairs by exploiting complementary information across views. To
attain superior performance, many methods have prioritized designing complex
modules to fuse similar information across views, yet overlooking the
importance of intra-view information for high-resolution reconstruction. It
also leads to problems of wrong texture in recovered images. To address this
issue, we explore the interdependencies between various hierarchies from
intra-view and propose a novel method, named Cross-View-Hierarchy Network for
Stereo Image Super-Resolution (CVHSSR). Specifically, we design a
cross-hierarchy information mining block (CHIMB) that leverages channel
attention and large kernel convolution attention to extract both global and
local features from the intra-view, enabling the efficient restoration of
accurate texture details. Additionally, a cross-view interaction module (CVIM)
is proposed to fuse similar features from different views by utilizing
cross-view attention mechanisms, effectively adapting to the binocular scene.
Extensive experiments demonstrate the effectiveness of our method. CVHSSR
achieves the best stereo image super-resolution performance than other
state-of-the-art methods while using fewer parameters. The source code and
pre-trained models are available at this https URL

本研究提出了一种名为 Cross-View-Hierarchy Network 的新方法，其中包括了利用通道关注和大核卷积关注从 intra-view 中提取全局和局部特征的交叉层次信息挖掘块（CHIMB），以及通过利用跨视图注意机制融合不同视图的相似特征的交叉视图交互模块（CVIM），并证明了 该方法比其他最先进的方法具有更好的立体图像超分辨率性能，且使用的参数更少。