  Image analysis methods that are based on exact blur values are faced with the
computational complexities due to blur measurement error. This atmosphere
encourages scholars to look for handcrafted and learned features for finding
depth from a single image. This paper introduces a novel exact realization for
blur measures on digital images and implements it on a new measure of defocus
Gaussian blur at edge points in Depth From Defocus (DFD) methods with the
potential to change this atmosphere. The experiments on real images indicate
superiority of the proposed measure in error performance over conventional
learned features in the state-of the-art single image based depth estimation
methods.

Computer Vision and Pattern Recognition