Low-light images often suffer from significant noise and detail loss, making it challenging to effectively distinguish signals from noise when processed directly in the spatial domain. To this end, we introduce frequency domain information to better distinguish high-frequency details from low-frequency components, thereby suppressing noise while enhancing details. The proposed method converts sRGB images into raw-RGB images by reversing the Image Signal Processor (ISP) pipeline to avoid unnecessary effects. A frequency information interaction processing unit is then designed to enhance low-frequency textures using high-frequency information and correct high-frequency data through low-frequency information to reduce structural deformation during restoration. The signal-to-noise ratio prior is introduced in the hidden layer to further remove noise. Experimental results show that the proposed method outperforms existing color and structure enhancement methods.

Considerable attention has been devoted recently to the problem of exploring and distilling factual knowledge from Pre-trained Language Models (PLMs). While continuous prompts are currently considered the best approach, determining the number and positions of continuous prompt vectors remains challenging to obtain the optimal prompts. In this paper, we introduce a new similarity-based algorithm that automatically searches for the best positions and number of continuous prompt vectors based on the similarity score between prompt representation and the relation representation for the final optimal prompts. The prompt optimization process is done before model training, making it efficient and less time-consuming. Our experiments demonstrate that our method significantly outperforms state-of-the-art methods in probing factual knowledge.