MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-Resolution

TL;DR

This paper introduces MM-RealSR, a novel unsupervised metric learning approach for interactive real-world super-resolution that effectively estimates degradation levels without explicit supervision, enabling better restoration performance.

Contribution

It proposes an unsupervised degradation estimation and a metric learning strategy with an anchor point to improve real-world super-resolution modulation.

Findings

Achieves superior modulation and restoration in real-world scenarios

Outperforms existing methods in handling unknown degradations

Demonstrates effectiveness through extensive experiments

Abstract

Interactive image restoration aims to restore images by adjusting several controlling coefficients, which determine the restoration strength. Existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. They usually suffer from a severe performance drop when the real degradation is different from their assumptions. Such a limitation is due to the complexity of real-world degradations, which can not provide explicit supervision to the interactive modulation during training. However, how to realize the interactive modulation in real-world super-resolution has not yet been studied. In this work, we present a Metric Learning based Interactive Modulation for Real-World Super-Resolution (MM-RealSR). Specifically, we propose an unsupervised degradation estimation strategy to estimate the degradation level in real-world scenarios. Instead of using known degradation levels as explicit supervision to the interactive mechanism, we propose a metric learning strategy to map the unquantifiable degradation levels in real-world scenarios to a metric space, which is trained in an unsupervised manner. Moreover, we introduce an anchor point strategy in the metric learning process to normalize the distribution of metric space. Extensive experiments demonstrate that the proposed MM-RealSR achieves excellent modulation and restoration performance in real-world super-resolution. Codes are available at https://github.com/TencentARC/MM-RealSR.