HiLLIE: Human-in-the-Loop Training for Low-Light Image Enhancement

TL;DR

HiLLIE introduces a human-in-the-loop training framework for low-light image enhancement that iteratively improves visual quality by incorporating human feedback and a tailored IQA model, leading to superior results.

Contribution

The paper presents a novel iterative training framework that integrates human guidance and a learned IQA model to enhance unsupervised low-light image enhancement.

Findings

Significant improvement in quantitative metrics.

Enhanced visual quality of low-light images.

Efficient use of minimal human annotations.

Abstract

Developing effective approaches to generate enhanced results that align well with human visual preferences for high-quality well-lit images remains a challenge in low-light image enhancement (LLIE). In this paper, we propose a human-in-the-loop LLIE training framework that improves the visual quality of unsupervised LLIE model outputs through iterative training stages, named HiLLIE. At each stage, we introduce human guidance into the training process through efficient visual quality annotations of enhanced outputs. Subsequently, we employ a tailored image quality assessment (IQA) model to learn human visual preferences encoded in the acquired labels, which is then utilized to guide the training process of an enhancement model. With only a small amount of pairwise ranking annotations required at each stage, our approach continually improves the IQA model's capability to simulate human visual assessment of enhanced outputs, thus leading to visually appealing LLIE results. Extensive experiments demonstrate that our approach significantly improves unsupervised LLIE model performance in terms of both quantitative and qualitative performance. The code and collected ranking dataset will be available at https://github.com/LabShuHangGU/HiLLIE.