Most approaches to estimate a scene’s 3D depth from a single image often model the point spread function (PSF) as a 2D Gaussian function. However, those method<span>s</span><span> are suffered ...Most approaches to estimate a scene’s 3D depth from a single image often model the point spread function (PSF) as a 2D Gaussian function. However, those method<span>s</span><span> are suffered from some noises, and difficult to get a high quality of depth recovery. We presented a simple yet effective approach to estimate exactly the amount of spatially varying defocus blur at edges, based on </span><span>a</span><span> Cauchy distribution model for the PSF. The raw image was re-blurred twice using two known Cauchy distribution kernels, and the defocus blur amount at edges could be derived from the gradient ratio between the two re-blurred images. By propagating the blur amount at edge locations to the entire image using the matting interpolation, a full depth map was then recovered. Experimental results on several real images demonstrated both feasibility and effectiveness of our method, being a non-Gaussian model for DSF, in providing a better estimation of the defocus map from a single un-calibrated defocused image. These results also showed that our method </span><span>was</span><span> robust to image noises, inaccurate edge location and interferences of neighboring edges. It could generate more accurate scene depth maps than the most of existing methods using a Gaussian based DSF model.</span>展开更多
This paper describes how the maximum blur radius affects the depth results by depth from the defocus(DFD)method based on liquid crystal(LC)lens.Boundary frequency is determined by the maximum blur radius.It is found t...This paper describes how the maximum blur radius affects the depth results by depth from the defocus(DFD)method based on liquid crystal(LC)lens.Boundary frequency is determined by the maximum blur radius.It is found that if the maximum blur radius used in the calculation is larger than the real value,the depth resolution obtained is reduced;on the other hand,if one smaller than the real value is used,the depth resolution in the middle range of the scene is increased,but errors occur in the near and far planes.Using the maximum blur radius close to the real one results in the best depth results.展开更多
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 para...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.展开更多
文摘Most approaches to estimate a scene’s 3D depth from a single image often model the point spread function (PSF) as a 2D Gaussian function. However, those method<span>s</span><span> are suffered from some noises, and difficult to get a high quality of depth recovery. We presented a simple yet effective approach to estimate exactly the amount of spatially varying defocus blur at edges, based on </span><span>a</span><span> Cauchy distribution model for the PSF. The raw image was re-blurred twice using two known Cauchy distribution kernels, and the defocus blur amount at edges could be derived from the gradient ratio between the two re-blurred images. By propagating the blur amount at edge locations to the entire image using the matting interpolation, a full depth map was then recovered. Experimental results on several real images demonstrated both feasibility and effectiveness of our method, being a non-Gaussian model for DSF, in providing a better estimation of the defocus map from a single un-calibrated defocused image. These results also showed that our method </span><span>was</span><span> robust to image noises, inaccurate edge location and interferences of neighboring edges. It could generate more accurate scene depth maps than the most of existing methods using a Gaussian based DSF model.</span>
基金This work was partially supported by Sichuan Science and Technology Program(Grant No.20YYJC4365).
文摘This paper describes how the maximum blur radius affects the depth results by depth from the defocus(DFD)method based on liquid crystal(LC)lens.Boundary frequency is determined by the maximum blur radius.It is found that if the maximum blur radius used in the calculation is larger than the real value,the depth resolution obtained is reduced;on the other hand,if one smaller than the real value is used,the depth resolution in the middle range of the scene is increased,but errors occur in the near and far planes.Using the maximum blur radius close to the real one results in the best depth results.
基金supported by the CAS FEA international partnership program for creative research teams
文摘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.
