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.

通过结合 CLIP 模型和相对简单的基线方法，我们提出了一种称为 CLIP-LUT 的简单结构，用于图像增强，并证明该方法能够获得令人满意的结果。