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Absolute Depth Estimation From a Single Defocused Image

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4 Author(s)
Jingyu Lin ; Autom. Dept., Tsinghua Univ., Beijing, China ; Xiangyang Ji ; Wenli Xu ; Qionghai Dai

Shape from defocus (SFD) is one of the most popular techniques in monocular 3D vision. While most SFD approaches require two or more images of the same scene captured at a fixed view point, this paper presents an efficient approach to estimate absolute depth from a single defocused image. Instead of directly measuring defocus level of each pixel, we propose to design a sequence of aperture-shape filters to segment a defocused image by defocus level. A boundary-weighted belief propagation algorithm is employed to obtain a smooth depth map. We also give an estimation of depth error. Extensive experiments show that our approach outperforms the state-of-the-art single-image SFD approaches both in precision of the estimated absolute depth and running time.

Published in:

IEEE Transactions on Image Processing  (Volume:22 ,  Issue: 11 )