Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution

TL;DR

This paper introduces MMSR, a self-supervised cross-modal super-resolution model that employs mutual modulation and cycle consistency to produce high-quality results without paired training data.

Contribution

The paper proposes a novel mutual modulation strategy with cross-domain adaptive filters and cycle consistency for self-supervised cross-modal super-resolution.

Findings

Achieves state-of-the-art performance on various tasks.

Effectively exploits cross-modal spatial dependencies.

Operates without paired training data.

Abstract

Self-supervised cross-modal super-resolution (SR) can overcome the difficulty of acquiring paired training data, but is challenging because only low-resolution (LR) source and high-resolution (HR) guide images from different modalities are available. Existing methods utilize pseudo or weak supervision in LR space and thus deliver results that are blurry or not faithful to the source modality. To address this issue, we present a mutual modulation SR (MMSR) model, which tackles the task by a mutual modulation strategy, including a source-to-guide modulation and a guide-to-source modulation. In these modulations, we develop cross-domain adaptive filters to fully exploit cross-modal spatial dependency and help induce the source to emulate the resolution of the guide and induce the guide to mimic the modality characteristics of the source. Moreover, we adopt a cycle consistency constraint to train MMSR in a fully self-supervised manner. Experiments on various tasks demonstrate the state-of-the-art performance of our MMSR.