Depth from Defocus for Mobile Cameras

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Depth from De-focus for Mobile phone camera

Output

OVERVIEW

Depth from Defocus (DFD) is a technique in which a depth image of a scene is reconstructed from multiple images with varying camera parameters from a single camera [1]. Parameters that affect defocus characteristics of an image are; distance to the focus plane, the focal length, and the depth of field which is controlled by the aperture size. I would like to explore and implement DFD methods on smartphones [2]. My aim is to display a captured scene with a some kind of 3D technique, i.e. parallax mapping. A use for this could be simple capturing of 3D photos of people or of sculptured art.

Technical Details

In the first stage I will implement a DFD method in MATLAB with image stacks taken by a stationary DSLR camera, i.e. no translation or parallax between images. This will give me a solid understanding of the mathematics behind the optics and the algorithms. In the second stage I will extend the above method to handle camera shake and alignment of images [3] in MATLAB and subsequently test the implementation with images taken by my iPhone.In the third stage I will implement the above method on my iPhone using OpenCV for iOS. I will experiment with the iOS Camera API to manually set the focus distance of the camera.If there is enough time I will implement an iPhone viewer for the image and its re- constructed depth map. Further work could be to investigate methods of improving the accuracy of the depth map by including shading information from the scene [4].

References

[1] J. Ens and P. Lawrence. An investigation of methods for determining depth from focus.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(2):97–108, Feb
1993.
[2] S. Suwajanakorn, C. Hernandez, and S. M. Seitz. Depth from focus with your mobile
phone. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
pages 3497–3506, June 2015.
[3] R. Ben-Ari. A unified approach for registration and depth in depth from defocus. IEEE
Transactions on Pattern Analysis and Machine Intelligence, 36(6):1041–1055, June 2014.
[4] C. Li, S. Su, Y. Matsushita, K. Zhou, and S. Lin. Bayesian depth-from-defocus with
shading constraints. IEEE Transactions on Image Processing, 25(2):589–600, Feb 2016.

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