Deep learning

Frame (networking)

Pattern recognition (psychology)

Noise reduction

Metric (unit)

Feature (linguistics)

Mathematics

Maximum likelihood

Psychotherapist

Politics

Maximum a posteriori estimation

Image (mathematics)

Statistics

Image restoration

Philosophy

Political science

Artificial intelligence

Psychology

Economics

Operations management

Representation (politics)

Computer science

Linguistics

Generality

Telecommunications

Image processing

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.

Deep Reparametrization of Multi-Frame Super-Resolution and Denoising