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.

翻译：由于光学系统的固有特性，光学成像系统通常受到景深的限制。因此，扩展景深（EDoF）是满足新兴视觉应用需求的一项基础性任务。为解决此问题，通常的做法是使用来自单一视点的多聚焦图像。该方法在固定视场条件下可以获得可接受的EDoF质量，但仅适用于静态场景，且视场受限且固定。变焦多视角图像作为一种新兴的数据类型，因其比多聚焦图像包含更多的视场信息，有潜力成为解决EDoF问题的新范式。为实现变焦多视角图像的EDoF，我们提出了一种端到端的EDoF方法，包括图像对齐、图像优化和图像融合。实验结果证明了所提方法的有效性。