Extending Depth of Field for Varifocal Multiview Images
This addresses the problem of limited depth of field in optical imaging for emerging visual applications, though it appears incremental as it builds on existing EDoF concepts with a new data type.
The paper tackled extending depth of field for varifocal multiview images, proposing an end-to-end method that includes image alignment, optimization, and fusion, with experimental results demonstrating its efficiency.
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.