用变焦多视角图像实现更广景深，提升动态场景成像质量。
Extending Depth of Field for Varifocal Multiview Images
- 通过图像对齐、优化与融合的端到端方法处理变焦多视角图像
- 相比传统单视角多焦点方法，支持动态场景且视场更广
- 适合需要大景深与灵活视场的实时视觉应用
光学成像系统因光学特性限制了景深范围，因此扩展景深（EDoF）是满足新兴视觉应用需求的基础任务。现有方法通常依赖单视角多焦点图像，在固定视场下可获得较好效果，但仅适用于静态场景且视场固定不变。新兴的变焦多视角图像数据蕴含比多焦点图像更多的视场信息，有潜力成为解决EDoF的新范式。为此，本文提出一种端到端的变焦多视角图像景深扩展方法，包含图像对齐、图像优化和图像融合三个步骤。实验结果表明，该方法在动态场景下能有效扩展景深并保持高质量输出。
原文摘要 · Abstract (English)
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.
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