Exact Blur Measure Outperforms Conventional Learned Features for Depth Finding

TL;DR

This paper introduces a novel exact blur measurement method for depth estimation from single images, demonstrating superior accuracy over traditional learned features in experimental tests.

Contribution

The paper presents a new exact blur measure implementation for digital images, improving depth estimation accuracy in Depth From Defocus methods.

Findings

Proposed measure outperforms conventional learned features in error performance.

Experimental results show improved depth estimation accuracy.

Method demonstrates potential to replace learned features in DFD applications.

Abstract

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.