Abstract:

Shape From Focus (SFF) is an important technique in the field of non-contact 3D reconstruction. Owing to the influence of the environment and the limitations of the camera, the image acquisition process inevitably generates noise, which affects the reconstruction accuracy. To address this problem, a high-precision, noise-resistant SFF method is proposed. First, the defocused sequence image is evaluated using the focus measure function to obtain the focus measure sequence image, and the initial depth map is obtained by locating the pixel focused position using the Gaussian fitting peak search method. Subsequently, the confidence map of the initial depth map is generated by measuring the confidence of the depth estimation based on the similarity between the focus measure curve and the grayscale curve of the pixel. Finally, a confidence map is used as the guide map to filter the initial depth map and obtain the optimized depth map. In the experiment, multiple sets of simulated defocused sequence images and real micro-defocused sequence images are used to verify the performance of the proposed method. The results demonstrate that the proposed method achieves excellent 3D reconstruction results in both simulation and real defocus sequences. In real data experiments, the root mean square error is reduced by at least 64.8% and 47.3%, respectively, and the correlation coefficient is improved by at least 2.18% and 6.35%, respectively, compared with the traditional methods. The proposed method has higher accuracy and stronger noise immunity, which can effectively improve the accuracy of the SFF.

Key words: Shape From Focus(SFF), 3D reconstruction, similarity, confidence, depth map, guide filtering

摘要：

聚焦形貌恢复是非接触式三维重建领域中的重要技术手段。由于环境的影响和相机本身的限制，图像采集过程中会不可避免地产生噪声信息，影响重建精度。针对该问题，提出一种高精度、抗噪声的聚焦形貌恢复算法。使用聚焦评价函数对离焦序列图像进行评价，得到聚焦评价序列图像，并使用高斯拟合峰值法定位像素聚焦位置获得初始深度图。在此基础上，通过像素的聚焦评价曲线与灰度曲线之间的相似度衡量深度估计置信度，生成初始深度图的置信图，并将置信图作为引导图对初始深度图进行引导滤波，得到优化后的深度图。使用多组仿真离焦序列图像与真实显微离焦序列图像对所提方法进行性能验证, 实验结果表明：所提方法在仿真与真实离焦序列中均能表现出优良的三维重建效果，在真实数据实验中，所提方法的所有指标均优于基于深度图优化的方法，与传统方法相比均方根误差分别降低64.8%和47.3%以上，相关系数分别提高2.18%和6.35%以上，具有更高的精度和更强的抗噪性，能有效提高聚焦形貌恢复精度。

关键词: 聚焦形貌恢复, 三维重建, 相似度, 置信度, 深度图, 引导滤波