Interactive Image Restoration

TL;DR

This paper introduces an interactive image restoration framework that combines image priors and human input iteratively, enhancing transparency, user control, and trust in the restoration process.

Contribution

It presents a novel interactive system that integrates human painting knowledge with image priors to improve restoration transparency and user engagement.

Findings

Positive objective and subjective evaluation results

Enhanced user trust and control in restoration process

Improved approachability of image restoration algorithms

Abstract

Machine learning and many of its applications are considered hard to approach due to their complexity and lack of transparency. One mission of human-centric machine learning is to improve algorithm transparency and user satisfaction while ensuring an acceptable task accuracy. In this work, we present an interactive image restoration framework, which exploits both image prior and human painting knowledge in an iterative manner such that they can boost on each other. Additionally, in this system users can repeatedly get feedback of their interactions from the restoration progress. This informs the users about their impact on the restoration results, which leads to better sense of control, which can lead to greater trust and approachability. The positive results of both objective and subjective evaluation indicate that, our interactive approach positively contributes to the approachability of restoration algorithms in terms of algorithm performance and user experience.