Abstract

We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks. Our approach is derived by introducing a learned error metric and a latent representation of the target image, which transforms the MAP objective to a deep feature space. The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction. Our approach thereby leverages the advantages of deep learning, while also benefiting from the principled multi-frame fusion provided by the classical MAP formulation. We validate our approach through comprehensive experiments on burst denoising and burst super-resolution datasets. Our approach sets a new state-of-the-art for both tasks, demonstrating the generality and effectiveness of the proposed formulation.

Paper

Goutam Bhat, Martin Danelljan, Fisher Yu, Luc Van Gool, Radu Timofte Deep Reparametrization of Multi-Frame Super-Resolution and Denoising ICCV 2021 Oral

Citation

@inproceedings{bhat2021deep,
  title     = {Deep Reparametrization of Multi-Frame Super-Resolution and Denoising},
  author    = {Bhat, Goutam and Danelljan, Martin and Yu, Fisher and Van Gool, Luc and Timofte, Radu},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      = {2021}
}