摘要
Depth from defocus(DFD),as a typical shape reconstruction method,has been widely researched in most recent years.However,all the existing DFD algorithms require at least two defocused images with different camera parameters.Unfortunately,in micro/nano manipulation,any change on visual sensor's parameters is absolutely forbidden.Therefore,a novel DFD method to reconstruct the shape of a nano grid on micro/nano scale is researched in this paper.First,the blurring imaging model is constructed with the relative blurring and the diffusion equation.Second,the relationship between depth and blurring is discussed from four aspects.Subsequently,depth measurement problem is transformed into an optimization issue which is solved using the gradient flow algorithm.Finally,experiment results and error analysis are conducted to show the feasibility and effectiveness of the proposed method.
Depth from defocus（DFD）,as a typical shape reconstruction method,has been widely researched in most recent years.However,all the existing DFD algorithms require at least two defocused images with different camera parameters.Unfortunately,in micro/nano manipulation,any change on visual sensor＇s parameters is absolutely forbidden.Therefore,a novel DFD method to reconstruct the shape of a nano grid on micro/nano scale is researched in this paper.First,the blurring imaging model is constructed with the relative blurring and the diffusion equation.Second,the relationship between depth and blurring is discussed from four aspects.Subsequently,depth measurement problem is transformed into an optimization issue which is solved using the gradient flow algorithm.Finally,experiment results and error analysis are conducted to show the feasibility and effectiveness of the proposed method.
基金
supported by the CAS FEA international partnership program for creative research teams
