用CLIP+强化学习，让简单图像处理自动优化低光图像亮度。
CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing
- 基于贝塞尔曲线的简单处理模块，动态调节全局色调。
- 利用CLIP文本嵌入设计奖励，强化学习自动调参，提升视觉质量。
- 速度快、效果好，适合高分辨率图像实时增强。
低光照图像增强对提升人眼感知和计算机视觉任务至关重要。本文针对零参考低光照图像增强的两个挑战：如何利用对比语言-图像预训练（CLIP）模型获得感知上‘优质’的图像，以及如何保持高分辨率图像的计算效率。我们提出基于强化学习的图像增强方法CURVE，其采用简单的图像处理模块，基于贝塞尔曲线调整全局图像色调，并通过强化学习迭代估计处理参数。奖励函数由CLIP文本嵌入设计。在低光照和多曝光数据集上的实验表明，相较于传统方法，CURVE在增强质量与处理速度方面均有显著提升。
原文摘要 · Abstract (English)
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.
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