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Image analysis methods that are based on exact blur values are faced with thecomputational complexities due to blur measurement error. This atmosphereencourages scholars to look for handcrafted and learned features for findingdepth from a single image. This paper introduces a novel exact realization forblur measures on digital images and implements it on a new measure of defocusGaussian blur at edge points in Depth From Defocus (DFD) methods with thepotential to change this atmosphere. The experiments on real images indicatesuperiority of the proposed measure in error performance over conventionallearned features in the state-of the-art single image based depth estimationmethods.

Exact Blur Measure Outperforms Conventional Learned Features for Depth Finding