A Review of Image Restoration Based on Deep Learning

Image restoration is a key task in the field of computer vision, which aims to recover high-quality clear images from degraded images. Degraded images may be produced for various reasons, such as noise, blurring, compression, etc. In recent years, the rapid development of deep learning technology has brought new breakthroughs in the field of image restoration, and various image restoration methods based on deep learning continue to emerge, and their performance is better than the previous GAN-based methods. Nevertheless, comprehensive and enlightening reviews of image restoration based on diffusion models are still scarce. This paper summarizes the latest research progress in image restoration in recent years, including cue learning, model scaling, generative prior, state space model, basic model and so on, and compares and analyzes their experimental results. Although the image restoration methods based on deep learning have achieved remarkable progress, there are still some challenges. Therefore, this paper analyzes the challenges in the field of image restoration and looks forward to the future research direction.