Multi-scale adaptive low-light image enhancement based on deep learning

Abstract

Existing low-light image enhancement (LLIE) technologies have difficulty balancing image quality and computational efficiency. In addition, they amplify the noise and artifacts of the original image when enhancing deep dark images. Therefore, this study proposes a multi-scale adaptive low-light image enhancement method based on deep learning. Specifically, feature extraction and noise reduction modules are designed. First, a more effective low-light enhancement effect is achieved by extracting the details of the dark area of an image. Depth extraction of the details of dark areas is realized through the design of a residual attention mechanism and nonlocal neural network in the UNet model to obtain a visual-attention map of the dark area. Second, the designed noise network obtains the real noise map of the low-light image. Subsequently, the enhanced network uses the dark area visual-attention and noise maps in conjunction with the original low-light image as inputs to adaptively realize LLIE. The LLIE results using the proposed network achieve excellent performance in terms of color, tone, contrast, and detail. Finally, quantitative and visual experiments on multiple test benchmark datasets demonstrate that the proposed method is superior to current state-of-the-art methods in terms of dark area details, image quality enhancement, and image noise reduction. The results of this study can help to address the real world challenges of low-light image quality, such as low contrast, poor visibility, and high noise levels.