The field of image and video restoration is rapidly advancing, with a focus on developing more efficient and effective methods for improving the quality of degraded images and videos. Recent research has explored the use of diffusion models, autoencoders, and other deep learning architectures to achieve state-of-the-art results in tasks such as super-resolution, deblurring, and low-light image enhancement. Notably, the development of single-step diffusion models and conditional flow matching models has shown great promise in achieving high-quality results while reducing computational complexity. Additionally, the use of patch-based content consistency adapters and cyclic self-supervised diffusion frameworks has enabled the creation of high-resolution images with precise content consistency and prompt alignment. Overall, the field is moving towards more efficient, scalable, and perceptually accurate methods for image and video restoration.

Noteworthy papers include: HeadsUp, which proposes a single-step diffusion model for portrait image super-resolution, achieving state-of-the-art performance on the PortraitISR task. FlashVSR, which introduces a diffusion-based one-step streaming framework for real-time video super-resolution, achieving state-of-the-art performance with up to 12x speedup over prior models. Cyclic Self-Supervised Diffusion, which proposes a framework for high-field MRI synthesis from low-field MRI data, achieving state-of-the-art performance and preserving anatomical fidelity.