Stereo image super-resolution utilizes the cross-view complementary
information brought by the disparity effect of left and right perspective
images to reconstruct higher-quality images. Cascading feature extraction
modules and cross-view feature interaction modules to make use of the
information from stereo images is the focus of numerous methods. However, this
adds a great deal of network parameters and structural redundancy. To
facilitate the application of stereo image super-resolution in downstream
tasks, we propose an efficient Multi-Level Feature Fusion Network for
Lightweight Stereo Image Super-Resolution (MFFSSR). Specifically, MFFSSR
utilizes the Hybrid Attention Feature Extraction Block (HAFEB) to extract
multi-level intra-view features. Using the channel separation strategy, HAFEB
can efficiently interact with the embedded cross-view interaction module. This
structural configuration can efficiently mine features inside the view while
improving the efficiency of cross-view information sharing. Hence, reconstruct
image details and textures more accurately. Abundant experiments demonstrate
the effectiveness of MFFSSR. We achieve superior performance with fewer
parameters. The source code is available at this https URL

我们提出了一种高效的轻量级立体图像超分辨率多级特征融合网络（MFFSSR），通过利用混合注意力特征提取块（HAFEB）提取多级视角内部特征，并使用通道分离策略与嵌入的视角交互模块有效地进行交互，该结构配置能够在改善跨视角信息共享效率的同时有效挖掘视图内的特征，从而更准确地重构图像细节和纹理。通过丰富的实验证明了 MFFSSR 的有效性，我们在更少的参数下实现了卓越的性能。源代码可在此 URL 找到。