Yinxue Zhang, Zhenhong Jia, Haijun Jiang, Zijian Liu
{"title":"基于鲁棒误差函数和粒子群优化- bp神经网络的图像恢复","authors":"Yinxue Zhang, Zhenhong Jia, Haijun Jiang, Zijian Liu","doi":"10.1109/ICNC.2008.140","DOIUrl":null,"url":null,"abstract":"A new method for image restoration based on robust error function and BP neural network optimized with particle swarm optimization (PSO) is proposed in this paper. In this technique, BP neural network uses a robust error function as its error function, and then the neural network optimized with PSO. This method can minimize an evaluation function established based on an observed image. The proposed method takes into consideration point spread function (PSF) blurring as well as an additive random noise and obtains restoration image with more preserved image details. Experimental results demonstrate that the proposed new method can have a very high quality both in the visual qualitative performance and the quantitative performance than the traditional algorithms.","PeriodicalId":6404,"journal":{"name":"2008 Fourth International Conference on Natural Computation","volume":"30 1","pages":"640-644"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Image Restoration Based on Robust Error Function and Particle Swarm Optimization-BP Neural Network\",\"authors\":\"Yinxue Zhang, Zhenhong Jia, Haijun Jiang, Zijian Liu\",\"doi\":\"10.1109/ICNC.2008.140\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new method for image restoration based on robust error function and BP neural network optimized with particle swarm optimization (PSO) is proposed in this paper. In this technique, BP neural network uses a robust error function as its error function, and then the neural network optimized with PSO. This method can minimize an evaluation function established based on an observed image. The proposed method takes into consideration point spread function (PSF) blurring as well as an additive random noise and obtains restoration image with more preserved image details. Experimental results demonstrate that the proposed new method can have a very high quality both in the visual qualitative performance and the quantitative performance than the traditional algorithms.\",\"PeriodicalId\":6404,\"journal\":{\"name\":\"2008 Fourth International Conference on Natural Computation\",\"volume\":\"30 1\",\"pages\":\"640-644\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Fourth International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2008.140\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Fourth International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2008.140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image Restoration Based on Robust Error Function and Particle Swarm Optimization-BP Neural Network
A new method for image restoration based on robust error function and BP neural network optimized with particle swarm optimization (PSO) is proposed in this paper. In this technique, BP neural network uses a robust error function as its error function, and then the neural network optimized with PSO. This method can minimize an evaluation function established based on an observed image. The proposed method takes into consideration point spread function (PSF) blurring as well as an additive random noise and obtains restoration image with more preserved image details. Experimental results demonstrate that the proposed new method can have a very high quality both in the visual qualitative performance and the quantitative performance than the traditional algorithms.