The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack

TL;DR

This paper introduces a novel PADMM-based framework for depth estimation from focal stacks, significantly improving accuracy and convergence speed over existing methods, thereby enhancing smartphone refocusing effects.

Contribution

The paper proposes a new PADMM-based approach for depth from focal stacks that offers higher accuracy and faster convergence than current state-of-the-art methods.

Findings

Outperforms five existing methods in structural accuracy

Achieves faster convergence in optimization process

Demonstrates improved depth map quality on 21 focal stack datasets

Abstract

Post capture refocusing effect in smartphone cameras is achievable by using focal stacks. However, the accuracy of this effect is totally dependent on the combination of the depth layers in the stack. The accuracy of the extended depth of field effect in this application can be improved significantly by computing an accurate depth map which has been an open issue for decades. To tackle this issue, in this paper, a framework is proposed based on Preconditioned Alternating Direction Method of Multipliers (PADMM) for depth from the focal stack and synthetic defocus application. In addition to its ability to provide high structural accuracy and occlusion handling, the optimization function of the proposed method can, in fact, converge faster and better than state of the art methods. The evaluation has been done on 21 sets of focal stacks and the optimization function has been compared against 5 other methods. Preliminary results indicate that the proposed method has a better performance in terms of structural accuracy and optimization in comparison to the current state of the art methods.