CLIP Guided Image-perceptive Prompt Learning for Image Enhancement
This work addresses image enhancement for computer vision applications, but it is incremental as it builds on existing LUT and CLIP methods.
The paper tackles image enhancement by proposing CLIP-LUT, a method that uses CLIP-guided prompt learning to distinguish image quality and steer a network combining three LUTs, achieving satisfactory results with a simple structure.
Image enhancement is a significant research area in the fields of computer vision and image processing. In recent years, many learning-based methods for image enhancement have been developed, where the Look-up-table (LUT) has proven to be an effective tool. In this paper, we delve into the potential of Contrastive Language-Image Pre-Training (CLIP) Guided Prompt Learning, proposing a simple structure called CLIP-LUT for image enhancement. We found that the prior knowledge of CLIP can effectively discern the quality of degraded images, which can provide reliable guidance. To be specific, We initially learn image-perceptive prompts to distinguish between original and target images using CLIP model, in the meanwhile, we introduce a very simple network by incorporating a simple baseline to predict the weights of three different LUT as enhancement network. The obtained prompts are used to steer the enhancement network like a loss function and improve the performance of model. We demonstrate that by simply combining a straightforward method with CLIP, we can obtain satisfactory results.