Adaptive deformation correction of depth from defocus for object reconstruction.

The accuracy of three-dimensional object reconstruction using depth from defocus (DfD) can be severely reduced by elliptical lens deformation. This paper presents two correction methods, correction by deformation cancellation (CDC) and correction by least squares fit (CLSF). CDC works by subtracting the current deformed depth value by a prestored deformed value, and CLSF by mapping the deformed values to the expected values. Each method is followed by a smoothing algorithm to address the low-texture problem of DfD. Experiments using four DfD methods on real images show that the proposed methods effectively and efficiently eliminate the deformation.

Rational-operator-based depth-from-defocus approach to scene reconstruction.

This paper presents a rational-operator-based approach to depth from defocus (DfD) for the reconstruction of three-dimensional scenes from two-dimensional images, which enables fast DfD computation that is independent of scene textures. Two variants of the approach, one using the Gaussian rational operators (ROs) that are based on the Gaussian point spread function (PSF) and the second based on the generalized Gaussian PSF, are considered. A novel DfD correction method is also presented to further improve the performance of the approach. Experimental results are considered for real scenes and show that both approaches outperform existing RO-based methods.

Measurement of the point-spread function of a noisy imaging system.

The averaged point-spread function (PSF) estimation of an image acquisition system is important for many computer vision applications, including edge detection and depth from defocus. The paper compares several mathematical models of the PSF and presents an improved measurement technique that enables subpixel estimation of 2D functions. New methods for noise suppression and uneven illumination modeling were incorporated. The PSF was computed from an ensemble of edge-spread function measurements. The generalized Gaussian was shown to be an 8 times better fit to the estimated PSF than the Gaussian and a 14 times better fit than the pillbox model.