Yi-tao Liang, Meng Zhang, Kui-bin Zhao, Yongzhi Li
{"title":"基于对比度增强的雾霾图像移动窗阈值分割算法","authors":"Yi-tao Liang, Meng Zhang, Kui-bin Zhao, Yongzhi Li","doi":"10.1109/SKIMA.2016.7916247","DOIUrl":null,"url":null,"abstract":"Under the bad weather, scattering of atmospheric particles lead to the degradation of image quality. And then the later image threshold segmentation is affected. We propose a moving-window threshold segmentation algorithm based on contrast enhancement. According to the characteristics of gray levels and by way of different histogram enhancement, the image contrast can be effectively improved. Moving-window threshold segmentation can reconstruct image gray space. In accordance with the certain rules and artificial selection of a small piece of Am XAn, threshold segmentation of sub-block can be done. Then the threshold segmentation of the whole image can be obtained through progressive scan from top to bottom. Then, by combining the split result together and smoothing the image block adjacent joint, the final image segmentation is obtained. The experimental results show that image gray histogram completely enhances the haze image and efficiently restrains the noise.","PeriodicalId":417370,"journal":{"name":"2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Haze image moving window threshold segmentation algorithm based on contrast enhancement\",\"authors\":\"Yi-tao Liang, Meng Zhang, Kui-bin Zhao, Yongzhi Li\",\"doi\":\"10.1109/SKIMA.2016.7916247\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Under the bad weather, scattering of atmospheric particles lead to the degradation of image quality. And then the later image threshold segmentation is affected. We propose a moving-window threshold segmentation algorithm based on contrast enhancement. According to the characteristics of gray levels and by way of different histogram enhancement, the image contrast can be effectively improved. Moving-window threshold segmentation can reconstruct image gray space. In accordance with the certain rules and artificial selection of a small piece of Am XAn, threshold segmentation of sub-block can be done. Then the threshold segmentation of the whole image can be obtained through progressive scan from top to bottom. Then, by combining the split result together and smoothing the image block adjacent joint, the final image segmentation is obtained. The experimental results show that image gray histogram completely enhances the haze image and efficiently restrains the noise.\",\"PeriodicalId\":417370,\"journal\":{\"name\":\"2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SKIMA.2016.7916247\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SKIMA.2016.7916247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Haze image moving window threshold segmentation algorithm based on contrast enhancement
Under the bad weather, scattering of atmospheric particles lead to the degradation of image quality. And then the later image threshold segmentation is affected. We propose a moving-window threshold segmentation algorithm based on contrast enhancement. According to the characteristics of gray levels and by way of different histogram enhancement, the image contrast can be effectively improved. Moving-window threshold segmentation can reconstruct image gray space. In accordance with the certain rules and artificial selection of a small piece of Am XAn, threshold segmentation of sub-block can be done. Then the threshold segmentation of the whole image can be obtained through progressive scan from top to bottom. Then, by combining the split result together and smoothing the image block adjacent joint, the final image segmentation is obtained. The experimental results show that image gray histogram completely enhances the haze image and efficiently restrains the noise.