Superpixel-based depth map estimation using defocus blur

Depth from defocus (DFD) technique calculates the blur amount in images considering that the depth and defocus blur are related to each other. Existing blur estimation methods generally compute the blur at edge locations and solve an optimization problem to propagate the blur from edges to all image...

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Bibliographic Details
Main Authors: Mahmoudpour, Saeed, Manbae Kim
Format: Conference Proceeding
Language:English
Subjects:
Defocus blur
depth map
image processing
optimization
superpixel
Online Access:Request full text
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Description
Summary:Depth from defocus (DFD) technique calculates the blur amount in images considering that the depth and defocus blur are related to each other. Existing blur estimation methods generally compute the blur at edge locations and solve an optimization problem to propagate the blur from edges to all image pixels. Solving the pixel-based optimization problem is time-consuming and it is the performance bottleneck of current approaches. Moreover, the generated depth maps are not consistent in textured areas and the blur estimation may be incorrect in the regions with soft shadows. We address these problems by proposing a superpixel-based blur estimation method. Experimental results show that our superpixel-based method is faster than pixel-based blur estimation and can improve depth data on textured regions and soft shadows.
ISSN:2381-8549
DOI:10.1109/ICIP.2016.7532832