Image enhancement algorithm for non-uniform illumination in underground mines

Abstract

Due to the non-uniform distribution of lighting systems and the presence of a large amount of dust and mist in the environment during the underground video collection process, there are problems with local light overexposure, insufficient brightness, low contrast, and weak edge information in the monitoring image. In order to solve the above problems, an image enhancement algorithm for non-uniform illumination in underground mines is proposed. This algorithm is based on the improvement of Retinex-Net network structure, which includes three parts: non-uniform illumination suppression module (NLSM), illumination decomposition module (LDM), and image enhancement module (IEM). Among them, NLSM suppresses local non-uniform illumination of artificial light sources in the image. LDM decomposes the image into light and reflection layers. IEM enhances the illumination layer of the image, undergoes gamma correction, and ultimately obtains the enhanced image. Resnet is adopted as the infrastructure of the network in both NLSM and LDM. The channel attention module and spatial attention module in the convolutional attention mechanism are sequentially introduced to enhance the attention to image lighting features and the efficiency of feature selection. The experimental results show the following points. ① MBLLEN, RUAS, zeroDCE, zeroDCE++, Retinex−Net, KinD++, and non-uniform illumination image enhancement algorithms are selected to enhance and qualitatively analyze images in various scenarios (underground transportation environment, single light source roadway, multi light source roadway, ore scenario). The analysis results indicate that non-uniform illumination image enhancement algorithms can avoid excessive enhancement of artificial light source areas. There is no halo or blurring phenomenon in the light source area, and colors are not prone to color deviation. The contrast is moderate, and the visual effect of the image is more realistic. ② The information entropy (IE), average gradient (AG), standard deviation (SD), naturalness image quality evaluator (NIQE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) are selected as evaluation indicators to quantitatively compare the quality of image enhancement images. The non-uniform illumination image enhancement algorithm is also in a relatively leading position in various scenarios. ③ The ablation experimental results show the non-uniform illumination image enhancement algorithm achieves optimal results on three evaluation indicators: NIQE, SSIM, and PSNR.

Keywords

• underground low light image
• non uniform illumination images
• image enhancement
• retinex-net
• image brightness
• residual network
• attention mechanism