吉林大学学报（信息科学版）2024，Vol.42Issue(6)：1058-1065,8.
基于均值滤波的遥感模糊图像对比度增强方法
Adaptive Multi-Threshold Image Segmentation Based on Deep Learning and Potential Function Clustering
摘要
Abstract
In order to improve the contrast enhancement effect of remote sensing blurred images and increase clarity,a method based on mean filtering for remote sensing blurred image contrast enhancement is proposed.Firstly,a fast median adaptive mean filtering algorithm is used to denoise the entire remote sensing blurred image.Secondly,combining the fractal self-similarity feature of remote sensing image edge and the change of gray scale gradient,the edge points of the image are extracted.On this basis,the whole area of the image is divided into bright areas and dim areas.Finally,the detail preserving mapping algorithm and perceptual contrast mapping method are used to enhance the contrast of the two regions,respectively,and the overall contrast of the remote sensing blurred image achieving color restoration of the image.The experimental results show that the proposed method can effectively denoise images,with an absolute mean difference of less than 0.85,and exhibits good performance in enhancing image contrast and clarity.关键词
均值滤波/遥感图像/对比度增强/去噪/边缘提取/色彩还原Key words
mean filtering/remote sensing image/contrast enhancement/denoising/edge extraction/color restoration分类
信息技术与安全科学引用本文复制引用
张艳晓..基于均值滤波的遥感模糊图像对比度增强方法[J].吉林大学学报（信息科学版）,2024,42(6):1058-1065,8.基金项目
陕西省教育厅科研计划基金资助项目(21JK0300) （21JK0300）
