Lokesh babu Gangula, G. Srikanth, Ch. Naveen, V. Satpute
{"title":"通过图像训练改善自动驾驶汽车的视觉","authors":"Lokesh babu Gangula, G. Srikanth, Ch. Naveen, V. Satpute","doi":"10.1109/SCEECS.2018.8546979","DOIUrl":null,"url":null,"abstract":"Driving cars in rainy situations lead to many accidents. This is the major issue with the automated cars and hence they are not promoted a lot. So in order to enhance the safety measure, we implemented this work of removing the rain components from a captured image during rainy situations. The rain components are removed from that image based on the rain characteristics. The colored image is divided into high frequency and low-frequency parts so that the high-frequency part consists of most of the rain components. Then by using Dictionary learning method the rain components are extracted from the high-frequency part. To extract more non-rain details we use Sensitivity of variance of color channels(SVCC).Finally, the non-rain component part and low-frequency part are combined to get the image without rain.","PeriodicalId":446667,"journal":{"name":"2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Vision Improvement in Automated Cars by Image Deraining\",\"authors\":\"Lokesh babu Gangula, G. Srikanth, Ch. Naveen, V. Satpute\",\"doi\":\"10.1109/SCEECS.2018.8546979\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Driving cars in rainy situations lead to many accidents. This is the major issue with the automated cars and hence they are not promoted a lot. So in order to enhance the safety measure, we implemented this work of removing the rain components from a captured image during rainy situations. The rain components are removed from that image based on the rain characteristics. The colored image is divided into high frequency and low-frequency parts so that the high-frequency part consists of most of the rain components. Then by using Dictionary learning method the rain components are extracted from the high-frequency part. To extract more non-rain details we use Sensitivity of variance of color channels(SVCC).Finally, the non-rain component part and low-frequency part are combined to get the image without rain.\",\"PeriodicalId\":446667,\"journal\":{\"name\":\"2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCEECS.2018.8546979\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCEECS.2018.8546979","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Vision Improvement in Automated Cars by Image Deraining
Driving cars in rainy situations lead to many accidents. This is the major issue with the automated cars and hence they are not promoted a lot. So in order to enhance the safety measure, we implemented this work of removing the rain components from a captured image during rainy situations. The rain components are removed from that image based on the rain characteristics. The colored image is divided into high frequency and low-frequency parts so that the high-frequency part consists of most of the rain components. Then by using Dictionary learning method the rain components are extracted from the high-frequency part. To extract more non-rain details we use Sensitivity of variance of color channels(SVCC).Finally, the non-rain component part and low-frequency part are combined to get the image without rain.