Depth from Defocus Estimation in Spatial Domain

2006

SPIE Proceedings

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.

Section: Introductionmentioning

confidence: 99%

Depth-from-defocus: blur equalization technique

Tao

2006

SPIE Proceedings

“…In the case of object surfaces normal to the direction of view, blurring is modeled by a shift-invariant/convolution operation. This case has been addressed through both frequency domain and spatial domain techniques ( [7], [9], [8], [6], [10]). Recently, the problem of 3D shape recovery from space/shift-variant defocused images has been investigated by several researchers.…”

Section: Introductionmentioning

confidence: 99%

A new approach to 3D shape recovery of local planar surface patches from shift-variant blurred images

2008 19th International Conference on Pattern Recognition

Kang

2008

“…Deterministic algorithms can be classified as frequency domain approaches 3,4 and spatial domain approaches. [5][6][7] The frequency domain approaches are generally computation expensive and yield lower depth-map density. While spatial domain approaches only use a small image region, thus require less computation and generate piecewise depth-map.…”

Section: Introductionmentioning

confidence: 99%

Depth and focused image recovery from defocused images for cameras operating in macro mode

Kang

2007

SPIE Proceedings

Depth From Defocus (DFD) is a depth recovery method that needs only two defocused images recorded with different camera settings. In practice, this technique is found to have good accuracy for cameras operating in Normal Mode. In this paper, we present new algorithms to extend the DFD method to cameras working in Macro Mode used for very close objects in a distance range of 5 cm to 20 cm. We adopted a new lens position setting suitable for Macro Mode to avoid serious blurring. We also developed a new calibration algorithm to normalize magnification of images captured with different lens positions. In some range intervals with high error sensitivity, we used an additional image to reduce the error caused by drastic change of lens settings. After finding the object depth, we used the corresponding blur parameter for computing the focused image through image restoration, which is termed as "soft-focusing". Experimental results on high-end digital camera show that the new algorithms significantly improve the accuracy of DFD in the Macro Mode. In terms of focusing accuracy, the RMS error is about 15 lens steps out of 1500 steps, which is around 1%.