Shape From Focus Based on Depth Estimation Confidence
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.
Shape From Focus(SFF)3D reconstructionsimilarityconfidencedepth mapguide filtering
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