CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing
This work addresses low-light image enhancement for computer vision applications, but it is incremental as it builds on existing CLIP and reinforcement learning techniques.
The paper tackled zero-reference low-light image enhancement by using CLIP-guided reinforcement learning to adjust global image tone via Bézier curves, achieving improved enhancement quality and faster processing speed compared to conventional methods.
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.