Depth from Defocus via Direct Optimization

TL;DR

This paper presents a global optimization method for depth from defocus that leverages alternating minimization, enabling high-resolution depth estimation with promising results on benchmark datasets.

Contribution

It introduces a feasible global optimization approach using alternating minimization for depth from defocus, outperforming some deep learning methods at high resolutions.

Findings

Effective depth estimation at higher resolutions.

Comparable or better results than prior methods on benchmarks.

Parallel computation enables efficient processing.

Abstract

Though there exists a reasonable forward model for blur based on optical physics, recovering depth from a collection of defocused images remains a computationally challenging optimization problem. In this paper, we show that with contemporary optimization methods and reasonable computing resources, a global optimization approach to depth from defocus is feasible. Our approach rests on alternating minimization. When holding the depth map fixed, the forward model is linear with respect to the all-in-focus image. When holding the all-in-focus image fixed, the depth at each pixel can be computed independently, enabling embarrassingly parallel computation. We show that alternating between convex optimization and parallel grid search can effectively solve the depth-from-defocus problem at higher resolutions than current deep learning methods. We demonstrate our approach on benchmark datasets with synthetic and real defocus blur and show promising results compared to prior approaches. Our code is available at github.com/hollyjackson/dfd.