Interest points as a focus measure

Abstract

In this paper, we propose a novel focus measure that is based on algorithms for interest point detection, particularly on the Fast Hessian detector. The proposed measure is compared with the energy of image gradient and sum-modified Laplacian that are commonly used as focus measures to test its reliability and performance. The measures have been tested on a newly created database containing 84 images (12 images for seven objects). Our algorithm proved to be a good focus measure satisfying all the requirements described in the paper, in some cases it outperformed the other two measures.

A Novel Autofocus Measure Based on Weighted Walsh-Hadamard Transform

“…This family of autofocus measures includes other kinds of methods not mentioned above, which utilize miscellaneous features of the image to measure the focus level, such as image curvature based method [29], the Steerable filters-based method [30], multi-scale quadrature filters based method [31], the autocorrelation based method [32], the interest point detection based method [33], and the inner energy based method [34].…”

Steerable local frequency based multispectral multifocus image fusion

“…As can be seen from the focus curves in the NIR spectrum, O3 performs better. For example, in case of 'Office Desk' image set O3 produces peak at image index 7 which is nearest to the index of image having highest focus from subjective assessment test given in [8]. In the TH spectrum there are a number of local maxima for each number of orientations due to limited resolution.…”

“…Zukal et al introduced the determination of focus measure via interest point detection [8]. An interest point in the image has significant local variations such as a corner or a junction.…”