Automatic focusing by correlative methods

Abstract

The possibility of focusing images by the autocorrelation function is explained. It can be shown that these techniques are less sensitive to disturbances by noise than others. Furthermore, focusing criteria derived from autocorrelation functions have different responses to image contrast. It has been shown that these focusing criteria can be determined using binary images and applying the laws of stochastic ergodic metrology. This leads to a large reduction in computing time. Moreover, an attempt was made to focus by means of binary images determined by simple segmentation. The experimental results show that such a focus criterion operates quite well. The criterion offers the advantage that brightness levels in the image can be chosen selectively for focusing.

Parametric logarithmic type image processing for contrast based auto-focus in extreme lighting conditions

“…In this paper we summarized our contribution to the advance of logarithmic-type image processing models and described their possible extensions with the precise purpose of preserving the mathematical property resulting (Nayar and Nakagawa, 1994) 84.8% 4.25 652 Vollath (1987) 92.3% 4.5 350 (Nayar and Nakagawa, 1994) 85.8% 5.5 464 Vollath (1987) 91.2% 3.66 188…”

“…Object in focus (a), object out of focus (b). (Nayar and Nakagawa, 1994) 80.3% 2.6 499 Vollath (1987) 88.0% 2.6 304 (2006) or Lee et al (2009), even though they rely on higher complexity they do not manage to overcome older solutions. The search technique used was the same for all FMFs, i.e., the hill climbing procedure.…”

“…The FMF encodes the relative proximity of the system to the focus position with respect to various lens positions. In practice, the FMF may be either of a derivative type (and then we look for the maximum) or in the family of correlation methods (Vollath, 1987), the case when the minimum is looked for. Once the measure is available, the FMF optimum with respect to the lens position must be searched for.…”

“…For comparison, we considered two other popular FMF measures: a derivative one-the classical sum of modified Laplacian and one from the correlation family, namely, Vollath F4 (Vollath, 1987). These are in general (Sun et al, 2005) perceived as the best performers.…”

The Weibull manifold in low-level image processing: An application to automatic image focusing

“…Such methods are usually based on the calculation of a "sharpness function" (SF), which is a real-valued estimate of the image focus. Commonly used sharpness functions in literature have been based on image derivatives [45,6,32,12,47,40], statistics [13,20,46,35,39] and Fourier transforms [24,44].…”