Mine Remote Sensing Image Denoising Algorithm Based on Lightweight YOLOX-S and Multi-threshold Eegmentation
There are a lot of noise points in mine remote sensing images,which brings great difficulties to the analysis and processing of subsequent images.A denoising algorithm of mine remote sensing image based on lightweight object detection model YOLOX-S and multi-threshold segmentation is proposed.Firstly,YOLOX-S model is used to detect the mine remote sensing image,and the location information of the mine target is obtained.Then,according to the characteristics of the mine tar-get,a multi-threshold segmentation method is designed to eliminate the noise in the image.The image is divided into several subregions,and each subregion is binarized with different thresholds.Finally,the binarized results of each subregion are com-bined to get the denoised image.The experimental results show that the algorithm can effectively remove the noise in the mine remote sensing image,and greatly improve the image quality while retaining the target features.In addition,due to the use of lightweight model and multi-threshold segmentation algorithm,the algorithm has a fast processing speed and low computing cost,which is suitable for large-scale image data processing tasks.
万方数据
维普
