HiLLIE: Human-in-the-Loop Training for Low-Light Image Enhancement
This work addresses the problem of generating visually appealing enhanced images in low-light conditions for users in computer vision applications, representing an incremental improvement through a novel training approach.
The paper tackles the challenge of aligning low-light image enhancement (LLIE) outputs with human visual preferences by proposing HiLLIE, a human-in-the-loop training framework that uses iterative stages with small amounts of pairwise ranking annotations to improve visual quality, resulting in significant quantitative and qualitative performance gains for unsupervised LLIE models.
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.