Ali Usman, Mahmood Muhammad Tariq
Computer Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, Cheonan, Republic of Korea.
Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, Cheonan, Republic of Korea.
Microsc Res Tech. 2021 Jul;84(7):1368-1374. doi: 10.1002/jemt.23716. Epub 2021 Jan 25.
Mostly, shape-from-focus (SFF) techniques do not consider any guidance or prior information from the input image sequence while improving the depth information. Consequently, the resultant depth maps may have inaccuracies due to missing the fine structural details. In this paper, we propose a depth enhancement method based on a novel guidance map and guided filtering. In the proposed method, first, a focus measure is applied on image sequence (volume) to compute the focus volume and an initial depth map is obtained by maximizing the focus measure in the optical direction. The guidance map is computed based on the correlation among the image volume and the focus volume along the optical axis. Finally, the improved depth map is obtained by applying guided filtering of initial depth by incorporating the weights from the suggested guidance map. Experiments were carried out using synthetic and real and microscopic image sequences of various objects. Experimental results have demonstrated the effectiveness of the proposed method and a reasonable improvement has been achieved in the quality of reconstructed depth maps.
大多数情况下，聚焦形状（SFF）技术在改进深度信息时并未考虑来自输入图像序列的任何引导或先验信息。因此，由于缺少精细的结构细节，生成的深度图可能存在不准确之处。在本文中，我们提出了一种基于新型引导图和引导滤波的深度增强方法。在所提出的方法中，首先，对图像序列（体数据）应用聚焦度量来计算聚焦体数据，并通过在光轴方向上最大化聚焦度量来获得初始深度图。引导图是基于图像体数据和沿光轴的聚焦体数据之间的相关性计算得出的。最后，通过结合来自所提出的引导图的权重对初始深度进行引导滤波，从而获得改进后的深度图。使用各种物体的合成、真实和微观图像序列进行了实验。实验结果证明了所提出方法的有效性，并且在重建深度图的质量上取得了合理的提升。
