  The large language model and high-level vision model have achieved impressive
performance improvements with large datasets and model sizes. However,
low-level computer vision tasks, such as image dehaze and blur removal, still
rely on a small number of datasets and small-sized models, which generally
leads to overfitting and local optima. Therefore, we propose a framework to
integrate large-model prior into low-level computer vision tasks. Just as with
the task of image segmentation, the degradation of haze is also
texture-related. So we propose to detect gray-scale coding, network channel
expansion, and pre-dehaze structures to integrate large-model prior knowledge
into any low-level dehazing network. We demonstrate the effectiveness and
applicability of large models in guiding low-level visual tasks through
different datasets and algorithms comparison experiments. Finally, we
demonstrate the effect of grayscale coding, network channel expansion, and
recurrent network structures through ablation experiments. Under the conditions
where additional data and training resources are not required, we successfully
prove that the integration of large-model prior knowledge will improve the
dehaze performance and save training time for low-level visual tasks.
