LEGAN: A low-light image enhancement generative adversarial network for industrial internet of smart-cameras

Abstract

The utilization of smart-cameras in the context of the Internet of Things (IoT) has become increasingly prevalent within smart workshops for performing in-situ quality inspection tasks. However, it is worth noting that these smart-cameras may encounter operational challenges when functioning under low-light conditions. The images acquired in such situation are severely degraded, resulting in the performance decline of the subsequent detection algorithms. Focusing on non-stationary noise compression and detail recovery, this paper constructs a novel enhancement model called LEGAN for the industrial internet of smart-cameras system. Firstly, the input undergoes a decomposition process into two branches using the Harr-wavelet technique. These branches are subsequently encoded independently by a series of compact residual blocks, facilitating effective noise suppression. Secondly, in order to enhance detail recovery, a feature selection module is meticulously designed to extract correlations between image foreground–background and low–high frequency signals, ultimately reconstructing a comprehensive feature map. This enables a multi-scale stepwise up-sampling approach that facilitates image recovery based on the reconstructed feature maps. Lastly, the training phase is supervised by an adversarial loss, comprising MSE loss, VGG loss, and discriminating loss, which ensures a harmonious balance between noise suppression and detail recovery. Comparative experiments clearly show the superiority of the LEGAN in terms of noise compression and detail recovery. Moreover, from an industrial practice perspective, the application of the proposed approach to yarn evenness inspection has proven to be highly effective, significantly enhancing detection accuracy in low-light environments.