The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack
This work addresses the long-standing open issue of depth estimation for enhancing extended depth of field effects in smartphone photography, representing an incremental improvement with specific gains in accuracy and optimization speed.
The paper tackled the problem of computing accurate depth maps from focal stacks to improve post-capture refocusing in smartphone cameras, proposing a framework based on Preconditioned Alternating Direction Method of Multipliers (PADMM) that showed better performance in structural accuracy and optimization compared to state-of-the-art methods, as evaluated on 21 focal stack sets against 5 other methods.
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.