Robust Focus Volume Regularization in Shape From Focus

Shape from focus (SFF) reconstructs 3D shape of the scene from a sequence of multi-focus images, and the quality of reconstructed shape mainly depends on the accuracy of image focus volume (FV). Traditional SFF techniques exhibit poor performance in preserving structural edges and fine details while...

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Bibliographic Details
Published in:IEEE transactions on image processing 2021, Vol.30, p.7215-7227
Main Authors: Ali, Usman, Mahmood, Muhammad Tariq
Format: Article
Language:English
Subjects:
Algorithms
Cameras
Convergence
depth map
focus measure
Frequency modulation
Image quality
Image reconstruction
Image sequences
non-convex optimization
Optimization
Regularization
Robustness
Sequences
Shape
Shape from focus (SFF)
Shape recognition
Solution space
Three-dimensional displays
volume regularization
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Summary:Shape from focus (SFF) reconstructs 3D shape of the scene from a sequence of multi-focus images, and the quality of reconstructed shape mainly depends on the accuracy of image focus volume (FV). Traditional SFF techniques exhibit poor performance in preserving structural edges and fine details while removing noisy artifacts, and mostly they do not incorporate any additional shape prior. Therefore, in this paper, we propose to refine FV by formulating an energy minimization framework that employs a nonconvex regularizer and incorporates two types of shape priors. The proposed regularizer is robust against noisy focus values. The first proposed shape prior is input image sequence and it is a single and static shape prior. While, the second shape prior corresponds to a series of shape priors. These shape priors are FVs which are iteratively obtained on-the-fly. Both of these shape priors constrain the solution space for output FV. We optimize nonconvex energy function through majorize-minimization algorithm which iteratively guarantees a local minimum and converges quickly. Experiments have been conducted to evaluate accuracy and convergence properties of the proposed method. Experimental results of synthetic and real image sequences demonstrate that our method achieves superior results in terms of ability to reconstruct accurate 3D shapes as compared to existing approaches.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2021.3100268