Depth-from-defocus: blur equalization technique

Tao Xian; Murali Subbarao

doi:10.1117/12.688615

12 October 2006 Depth-from-defocus: blur equalization technique

Tao Xian, Murali Subbarao

Proceedings Volume 6382, Two- and Three-Dimensional Methods for Inspection and Metrology IV; 63820E (2006) https://doi.org/10.1117/12.688615
Event: Optics East 2006, 2006, Boston, Massachusetts, United States

Abstract

A new spatial-domain Blur Equalization Technique (BET) is presented. BET is based on Depth-from-Defocus (DFD) technique. It relies on equalizing the blur or defocus of two different images recorded with different camera parameters. Also, BET facilitates modeling of images locally by higher order polynomials with lower series truncation errors. The accuracy of BET is further enhanced by discarding pixels with low Signal-to-Noise ratio by thresholding image Laplacians, and relying more on sharper of the two blurred images in estimating the blur parameters. BET is found to be superior to some of the best comparable DFD techniques in a large number of both simulation and actual experiments. Actual experiments used a large variety of objects including very low contrast digital camera test charts located at many different distances. In autofocusing experiments, BET gave an RMS error of 1.2% in lens position.

Citation Download Citation

Tao Xian and Murali Subbarao "Depth-from-defocus: blur equalization technique", Proc. SPIE 6382, Two- and Three-Dimensional Methods for Inspection and Metrology IV, 63820E (12 October 2006); https://doi.org/10.1117/12.688615

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