Multi-level fusion for burst super-resolution with deep permutation-invariant conditioning

Developing deep learning techniques for super-resolving bursts of images acquired by mobile cameras is a topic that has recently gained significant interest. This topic fits the general problem of learning-based multi-image super-resolution (SR), which, contrary to its sibling single-image SR, has so far received little attention despite its potential. In this work, we introduce a neural network architecture for burst SR, called MLB-FuseNet (Multi-Level Burst Fusion Network), that is capable of extracting features in a manner that is invariant to permutations in the burst and to progressively condition features extracted from a reference image. Permutation invariance is desirable as it is known that the order of images in a burst does not matter in this problem, but its study has so far been neglected. Moreover, we also introduce a module exploiting a polyphase decomposition to improve feature extraction from mosaiced raw images. Results show an improvement over the state of the art on the BurstSR dataset - a recent and popular benchmark for this problem.