Image deblurring is an important preprocessing step in the inspection and measurement applications of machine vision
systems. A computational algorithm and analysis are presented for a new approach to one-dimensional shift-variant
image deblurring. The new approach is based on a new mathematical transform that restates the traditional shift-variant
image blurring model in a completely local but exactly equivalent form. The new approach is computationally noniterative,
efficient, and permits very fine-grain parallel implementation. The theory of the new approach for onedimensional
shift-variant deblurring is presented. Further, its advantages in comparison with related approaches, and
experimental results are presented.
A new passive ranging technique named Robust Depth-from-Defocus (RDFD) is presented for autofocusing in digital
cameras. It is adapted to work in the presence of image shift and scale change caused by camera/hand/object motion.
RDFD is similar to spatial-domain Depth-from-Defocus (DFD) techniques in terms of computational efficiency, but it
does not require pixel correspondence between two images captured with different defocus levels. It requires
approximate correspondence between image regions in different image frames as in the case of Depth-from-Focus (DFF)
techniques. Theory and computational algorithm are presented for two different variations of RDFD. Experimental
results are presented to show that RDFD is robust against image shifts and useful in practical applications. RDFD also
provides insight into the close relation between DFF and DFD techniques.
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 a 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%.