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Image enhancement is a significant research area in the fields of computervision and image processing. In recent years, many learning-based methods forimage enhancement have been developed, where the Look-up-table (LUT) has provento be an effective tool. In this paper, we delve into the potential ofContrastive Language-Image Pre-Training (CLIP) Guided Prompt Learning,proposing a simple structure called CLIP-LUT for image enhancement. We foundthat the prior knowledge of CLIP can effectively discern the quality ofdegraded images, which can provide reliable guidance. To be specific, Weinitially learn image-perceptive prompts to distinguish between original andtarget images using CLIP model, in the meanwhile, we introduce a very simplenetwork by incorporating a simple baseline to predict the weights of threedifferent LUT as enhancement network. The obtained prompts are used to steerthe enhancement network like a loss function and improve the performance ofmodel. We demonstrate that by simply combining a straightforward method withCLIP, we can obtain satisfactory results.

CLIP Guided Image-perceptive Prompt Learning for Image Enhancement