Image Super Resolution Network

Image super-resolution (SR) networks aim to reconstruct high-resolution images from low-resolution inputs, a crucial task with applications ranging from medical imaging to drone vision. Current research emphasizes improving efficiency and robustness, focusing on architectures like Swin Transformers and variations of convolutional neural networks that incorporate attention mechanisms and quantization techniques to reduce computational cost while maintaining or improving image quality. These advancements are significant because they enable the deployment of SR technology on resource-constrained devices and improve the accuracy and detail of reconstructed images in diverse real-world scenarios.