Retinex Based Visual Image Enhancement Algorithm for Coal Mine Exploration Robots

Abstract

Due to the unique nature of coal mining environments, images captured in low illumination environments often have problems like low brightness, poor contrast, and loss of detail information, which seriously affects the quality of images and the information carried. In response to this issue, this study proposes a visual image enhancement algorithm for coal mine exploration robots based on Retinex. This method first decomposes low illumination images into light mapping and reflection mapping through the light smoothing loss function, and then enhances the former and denoises the latter through an improved Retex-net. Finally, the two are combined to output the enhanced image. The conclusion verifies that when the training set reaches 1000, the structural similarity values of the improved Retinex-Net algorithm, global illumination perception and detail preserving network, relative average generative adversarial network, and Retinex-Net are 0.98, 0.95, 0.89, and 0.88, respectively. When the iterations are 500, the accuracy of Retinex-Net algorithm, global illumination perception and detail preserving network, relative average generative adversarial network, and Retinex-U-Net algorithm are 0.78, 0.53, 0.38, and 0.31, respectively. The data indicates that the designed algorithm owns good performance and makes a positive contribution to improving the efficiency and safety of coal mine exploration work.