Stereo Image Super Resolution

Stereo image super-resolution (SR) aims to enhance the resolution of low-resolution image pairs from binocular vision systems, maintaining consistency between the two views. Current research focuses on developing efficient and accurate models, leveraging techniques like transformers and convolutional neural networks (CNNs), often incorporating attention mechanisms and multi-scale feature fusion to effectively utilize cross-view information and preserve fine details. These advancements are significant for improving the quality of images in various applications, such as autonomous driving, robotics, and medical imaging, where high-resolution stereo vision is crucial. The field is actively exploring lightweight architectures to enable real-time processing and improve the perceptual quality of the super-resolved images.