{"title":"Depth restoration from defocused images using simulated annealing","authors":"K. Prasad, R. Mammone","doi":"10.1109/ICPR.1990.118099","DOIUrl":null,"url":null,"abstract":"The recovery of depth from defocused images is formulated as a 3-D image restoration problem. A defocused image is modeled as the combinatorial outcome of the depths and intensities of the volume elements (voxels) of an opaque 3-D object. A large depth-of-field image is used to constrain the intensities of the voxels. The depths of voxels are estimated from a highly defocused image by using simulated annealing to solve a constrained optimization problem. It is concluded that the method provides a framework for high-resolution depth recovery from defocused images. The method is computationally-intensive; however, it is amenable to parallel processing and is well suited for small field-of-interest applications.<<ETX>>","PeriodicalId":135937,"journal":{"name":"[1990] Proceedings. 10th International Conference on Pattern Recognition","volume":"151 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1990-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1990] Proceedings. 10th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.1990.118099","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The recovery of depth from defocused images is formulated as a 3-D image restoration problem. A defocused image is modeled as the combinatorial outcome of the depths and intensities of the volume elements (voxels) of an opaque 3-D object. A large depth-of-field image is used to constrain the intensities of the voxels. The depths of voxels are estimated from a highly defocused image by using simulated annealing to solve a constrained optimization problem. It is concluded that the method provides a framework for high-resolution depth recovery from defocused images. The method is computationally-intensive; however, it is amenable to parallel processing and is well suited for small field-of-interest applications.<>