Remote sensing image dehazing method in mountaineering equipment

Abstract With the spread of national sports awareness, more people are fond of outdoor mountaineering. Intelligent mountaineering equipment can provide some safe and convenient services for outdoor mountaineering. However, the positioning accuracy is affected by the ability of remote sensing image recognition in the background. To address the haze interference in remote sensing image positioning, the Retinex algorithm is enhanced using an atmospheric scattering model. This improved Retinex algorithm adopts a multiscale retinal enhancement algorithm with color restoration to enhance images. The Gaussian kernel function plays a filtering role, while the guided filtering is used to improve image texture and details. Test data from background remote sensing images of mountaineering equipment are used to evaluate the algorithm. Results indicate that when the entire test set is used for calculations, the normalized information entropy of I-Retinex, Retinex, generative adversarial networks, and Alex models is 0.92. The median values of normalized mean squared error and mean absolute error are 0.13 and 0.15, respectively, outperforming the contrast defogging model. There is a negative correlation between the normalized peak signal-to-noise ratio and haze noise error in each model. When the entire test set is used, the normalized average gradient of I-Retinex is 0.87, significantly higher than the comparison models. However, the I-Retinex model developed in this study lacks optimal average computation time and memory consumption data. Experimental results demonstrate that the improved haze removal model effectively removes haze from remote sensing images, supporting the remote sensing image-related service functionalities of mountaineering equipment.