The computer vision community has developed numerous techniques for digitally restoring true scene information from single-view degraded photographs, an important yet extremely ill-posed task. In this work, we tackle image restoration from a different perspective by jointly denoising multiple photographs of the same scene. Our core hypothesis is that degraded images capturing a shared scene contain complementary information that, when combined, better constrains the restoration problem. To this end, we implement a powerful multi-view diffusion model that jointly generates uncorrupted views by extracting rich information from multi-view relationships. Our experiments show that our multi-view approach outperforms existing single-view image and even video-based methods on image deblurring and super-resolution tasks. Critically, our model is trained to output 3D consistent images, making it a promising tool for applications requiring robust multi-view integration, such as 3D reconstruction or pose estimation.
@article{arxiv.2503.14463,
title = {SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model},
author = {Yucheng Mao and Boyang Wang and Nilesh Kulkarni and Jeong Joon Park},
journal= {arXiv preprint arXiv:2503.14463},
year = {2025}
}