Low-Light Image Enhancement Network Guided by Illuminance Map

Images captured in low-light environment suffer from poor visibility, low contrast and color distortion due to the uneven illumination. Most of the existing low-light image enhancement methods have problems of over- or under-enhancement, which affects visual perception and subsequent object detection tasks. To address these problems, this paper proposed a low-light image enhancement network based on illumination map guidance. First, according to the grayscale distribution characteristics of the low-light images, the corresponding illumination map is constructed to measure the brightness and darkness of different areas of the low-light image; then, the illumination map is regarded as a guidance map and fed into the image enhancement network together with the low-light image to obtain the enhanced image. In addition, in order to solve the problem of insufficient training data, a data enhancement method based on inner loop and probability rotation is proposed to expand the number and diversity of training data samples; simultaneously, a histogram loss function is designed based on the idea of histogram matching to constrain and guide the training of the network to overcome the problem of uneven illumination in current image enhancement methods. Experimental results on synthetic dataset LOL and real images demonstrate that the proposed network achieves better subjective visual effects in low-light image enhancement. Compared with the classical RetinexNet method, the proposed method improves the objective quantitative indexes of PSNR and SSIM by 7.905dB and 0.328, respectively; moreover, the detection rate of the proposed network for subsequent object detection tasks can be improved by 10.17% to 17.19%. © 2024 Institute of Computing Technology. All rights reserved.