{"title":"基于粒子群算法的图像融合扩展数码相机的景深","authors":"V. Aslantaş, Rifat Kurban","doi":"10.1109/ISCE.2010.5523731","DOIUrl":null,"url":null,"abstract":"Images obtained by an optical camera are seriously affected from the limited depth of the field issue. That is, the objects located in-front-of or behind the focus of the camera are blurred. Everywhere-in-focus images can be obtained by combining focused regions of images which were captured by different focal settings. In this paper, an optimal region based spatial domain image fusion approach based on selecting sharper regions from the multi-focus source images is proposed. Size of the block type regions are optimized by using particle swarm optimization (PSO) algorithm. Quantitative and subjective experimental results of different image sets show that proposed method is better than traditional wavelet and Laplacian pyramid based and also genetic algorithm (GA) based methods.","PeriodicalId":403652,"journal":{"name":"IEEE International Symposium on Consumer Electronics (ISCE 2010)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Extending depth-of-field of a digital camera using particle swarm optimization based image fusion\",\"authors\":\"V. Aslantaş, Rifat Kurban\",\"doi\":\"10.1109/ISCE.2010.5523731\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Images obtained by an optical camera are seriously affected from the limited depth of the field issue. That is, the objects located in-front-of or behind the focus of the camera are blurred. Everywhere-in-focus images can be obtained by combining focused regions of images which were captured by different focal settings. In this paper, an optimal region based spatial domain image fusion approach based on selecting sharper regions from the multi-focus source images is proposed. Size of the block type regions are optimized by using particle swarm optimization (PSO) algorithm. Quantitative and subjective experimental results of different image sets show that proposed method is better than traditional wavelet and Laplacian pyramid based and also genetic algorithm (GA) based methods.\",\"PeriodicalId\":403652,\"journal\":{\"name\":\"IEEE International Symposium on Consumer Electronics (ISCE 2010)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Symposium on Consumer Electronics (ISCE 2010)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCE.2010.5523731\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Symposium on Consumer Electronics (ISCE 2010)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCE.2010.5523731","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extending depth-of-field of a digital camera using particle swarm optimization based image fusion
Images obtained by an optical camera are seriously affected from the limited depth of the field issue. That is, the objects located in-front-of or behind the focus of the camera are blurred. Everywhere-in-focus images can be obtained by combining focused regions of images which were captured by different focal settings. In this paper, an optimal region based spatial domain image fusion approach based on selecting sharper regions from the multi-focus source images is proposed. Size of the block type regions are optimized by using particle swarm optimization (PSO) algorithm. Quantitative and subjective experimental results of different image sets show that proposed method is better than traditional wavelet and Laplacian pyramid based and also genetic algorithm (GA) based methods.