PromptLNet: Region-Adaptive Aesthetic Enhancement via Prompt Guidance in Low-Light Enhancement Net
Journal:
arXiv
Published Date:
Mar 11, 2025
Abstract
Learning and improving large language models through human preference
feedback has become a mainstream approach, but it has rarely been applied to
the field of low-light image enhancement. Existing low-light enhancement
evaluations typically rely on objective metrics (such as FID, PSNR, etc.),
which often result in models that perform well objectively but lack aesthetic
quality. Moreover, most low-light enhancement models are primarily designed for
global brightening, lacking detailed refinement. Therefore, the generated
images often require additional local adjustments, leading to research gaps in
practical applications. To bridge this gap, we propose the following
innovations: 1) We collect human aesthetic evaluation text pairs and aesthetic
scores from multiple low-light image datasets (e.g., LOL, LOL2, LOM, DCIM, MEF,
etc.) to train a low-light image aesthetic evaluation model, supplemented by an
optimization algorithm designed to fine-tune the diffusion model. 2) We propose
a prompt-driven brightness adjustment module capable of performing fine-grained
brightness and aesthetic adjustments for specific instances or regions. 3) We
evaluate our method alongside existing state-of-the-art algorithms on
mainstream benchmarks. Experimental results show that our method not only
outperforms traditional methods in terms of visual quality but also provides
greater flexibility and controllability, paving the way for improved aesthetic
quality.
