Abstract: In low light images, the effective signal and random noise exhibit a similar sparse distribution in the transform domain. The denoised image is prone to staircase effects or pseudo edges, which can lead to noise artifacts and reduce the quality of the image. Therefore, an online dictionary learning algorithm is proposed to smooth and denoise low light images, and to improve the visual effect of the images. The grayscale transformation on low light images is Implemented to reduce random noise in the images. An adaptive low light image block partitioning strategy is designed which dynamically adjusts the size of image blocks based on local brightness information and texture features after grayscale transformation, to obtain image details and structural information. An online dictionary learning model is created. It sparsely represents partitioned image blocks, dynamically captures the time-varying characteristics of noise and detail features through real-time dictionary updates, adaptively separates effective signals and noise, suppresses noise artifacts while preserving signal structure, solves the problems of staircase effect and pseudo edges caused by similar sparse distribution in low light images, and achieves smooth denoising of low light images. The experimental results show that the proposed method has strong robustness and can effectively suppress low light image noise. The peak signal-to-noise ratio and structural similarity of the image are significantly improved. 

Key words: online dictionary learning, low light image, smooth denoising, grayscale transformation, image block