Computer Science ›› 2017, Vol. 44 ›› Issue (1): 32-36.doi: 10.11896/j.issn.1002-137X.2017.01.006

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Depth Estimation from Single Defocused Image Based on Gaussian-Cauchy Mixed Model

XUE Song and WANG Wen-jian   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Recovering the 3D depth of a scene from a single image is a difficult problem in the field of computer vision.Most methods for depth estimation from a single defocused image construct the point spread function by an 2D Gaussian or Cauchy distribution.However,reasons of blurred images in the real world are varied,so a simple Gaussian or Cauchy distribution function may be not the best approximation model.They are often influenced by noise and inaccurate edge location,and then a high quality depth estimation may be difficult to achieve.A Gaussian-Cauchy mixed distribution model was presented in this paper to re-blur the given defocused image,and two different degree blurred images were then obtained.We estimated the sparse depth map generated from the gradients ratio at edge locations by the two blurred images.In so doing,a full depth map can be recovered by matting Laplacian interpolation.Experimental results on some real images demonstrate that the proposed approach is effective and better than the two commonly used approaches.

Key words: Depth estimation,Defocus blur,GC-PSF

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