Super-resolution reconstruction aims to improve the resolution of low-resolution images. It involves algorithms that learn the relationship between low- and high-resolution image pairs, typically using deep learning techniques. For example, it can enhance the quality of old or blurred photos. New approaches, such as integrating parametric regularization techniques, have shown promise in generating high-quality reconstructed images, effectively restoring complex textures. These methods can be applied in fields like medical imaging, satellite imagery, and digital archiving.
