High Resolution Image Restoration

High-resolution image restoration aims to recover high-quality images from degraded versions, addressing issues like blur, noise, and compression artifacts. Current research heavily focuses on developing efficient deep learning models, particularly exploring state-space models and modified transformer architectures to overcome the computational limitations of traditional methods when dealing with large images. These advancements are crucial for improving various applications, including medical imaging, satellite imagery analysis, and enhancing the visual quality of digital media, by enabling faster and more accurate restoration of high-resolution images. The emphasis is on achieving state-of-the-art performance while significantly reducing computational cost and memory usage.