Abstract
Low-Light Image Enhancement (LLIE) is crucial for improving both human perception and computer vision tasks. This paper addresses two challenges in zero-reference LLIE: obtaining perceptually 'good' images using the Contrastive Language-Image Pre-Training (CLIP) model and maintaining computational efficiency for high-resolution images. We propose CLIP-Utilized Reinforcement learning-based Visual image Enhancement (CURVE). CURVE employs a simple image processing module which adjusts global image tone based on Bézier curve and estimates its processing parameters iteratively. The estimator is trained by reinforcement learning with rewards designed using CLIP text embeddings. Experiments on low-light and multi-exposure datasets demonstrate the performance of CURVE in terms of enhancement quality and processing speed compared to conventional methods.
Abstract (translated)
低光图像增强（LLIE）对于改善人类感知和计算机视觉任务至关重要。本文解决了零参考LLIE的两个挑战：使用对比语言-图像预训练（CLIP）模型获取感知上“良好”的图像，以及为高分辨率图像保持计算效率。我们提出了基于强化学习并利用CLIP的视觉图像增强方法（CURVE）。CURVE采用了一个简单的图像处理模块，该模块根据贝塞尔曲线调整全局图像色调，并通过迭代估计其处理参数。估计算法通过使用CLIP文本嵌入设计奖励的方式进行强化学习训练。 在低光和多曝光数据集上的实验表明，与传统方法相比，CURVE在增强质量和处理速度方面表现出色。
URL
https://arxiv.org/abs/2505.23102
