Summary of Solving Video Inverse Problems Using Image Diffusion Models, by Taesung Kwon et al.
Solving Video Inverse Problems Using Image Diffusion Models
by Taesung Kwon, Jong Chul Ye
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to solving inverse problems in video, specifically addressing spatio-temporal degradations. Building on the success of image diffusion models, the method treats the time dimension as the batch dimension and uses denoised spatio-temporal batches derived from each image diffusion model to solve optimization problems. The authors also introduce a batch-consistent diffusion sampling strategy that ensures consistency across batches by synchronizing stochastic noise components. Experimental results show state-of-the-art reconstructions for various video inverse problems. The proposed method, which combines batch-consistent sampling with simultaneous optimization of denoised spatio-temporal batches at each reverse diffusion step, is demonstrated to effectively address spatio-temporal degradations in video inverse problems. The authors’ approach leverages image diffusion models and tackles the challenges of training video diffusion models. Keywords: video inverse problems, diffusion model-based inverse problem solvers, decomposed diffusion sampler, batch-consistent diffusion sampling strategy, video super-resolution, deblurring, inpainting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about solving a type of math problem called an “inverse problem” in videos. The problem is that sometimes videos get distorted or degraded, and we want to restore them to their original quality. The authors came up with a new way to solve this problem by using something called diffusion models, which are usually used for images. They treated the time dimension of the video like it’s just another batch of images and used these diffusion models to solve the problem. They also added a special trick to make sure that all the different parts of the video work together well. The results show that their approach is very effective in restoring degraded videos. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Optimization » Super resolution
