Extending Depth of Field for Varifocal Multiview Images

TL;DR

This paper introduces an end-to-end method to extend the depth of field in varifocal multiview images, leveraging their richer field of view data to overcome limitations of traditional multi-focus approaches.

Contribution

The paper presents a novel end-to-end approach for EDoF in varifocal multiview images, including alignment, optimization, and fusion, expanding capabilities beyond static scene limitations.

Findings

The proposed method effectively extends depth of field in varifocal multiview images.

Experimental results show improved image quality and field of view.

The approach outperforms traditional multi-focus EDoF techniques.

Abstract

Optical imaging systems are generally limited by the depth of field because of the nature of the optics. Therefore, extending depth of field (EDoF) is a fundamental task for meeting the requirements of emerging visual applications. To solve this task, the common practice is using multi-focus images from a single viewpoint. This method can obtain acceptable quality of EDoF under the condition of fixed field of view, but it is only applicable to static scenes and the field of view is limited and fixed. An emerging data type, varifocal multiview images have the potential to become a new paradigm for solving the EDoF, because the data contains more field of view information than multi-focus images. To realize EDoF of varifocal multiview images, we propose an end-to-end method for the EDoF, including image alignment, image optimization and image fusion. Experimental results demonstrate the efficiency of the proposed method.