CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing

• URL: http://arxiv.org/abs/2505.23102v2
• Date: Tue, 08 Jul 2025 14:27:04 GMT
• Title: CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing
• Authors: Yuka Ogino, Takahiro Toizumi, Atsushi Ito,
• Abstract summary: Low-Light Image Enhancement (LLIE) is crucial for improving both human perception and computer vision tasks.<n>This paper addresses two challenges in zero-reference LLIE: obtaining perceptually 'good' images and maintaining computational efficiency for high-resolution images.<n>We propose CLIP-Utilized Reinforcement learning-based Visual image Enhancement (CURVE)
• Score: 0.5803309695504829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 B\'ezier 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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