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    多尺度代价聚合的多聚焦图像3维形貌重建框架

    Multi-Scale Cost Aggregation Framework for 3D Shape from Focus

    • 摘要: 多聚焦图像3维形貌重建旨在利用不同聚焦水平的图像序列恢复场景的3维结构信息. 现有的3维形貌重建方法大多从单一尺度对图像序列的聚焦水平进行评价,通过引入正则化或后处理方法引导重建过程,由于深度信息选择空间的局限性往往导致重建结果无法有效收敛. 针对上述问题,提出一种多尺度代价聚合的多聚焦图像3维形貌重建框架MSCAS(multi-scale cost aggregation framework for shape from focus),该框架首先引入非降采样的多尺度变换增加输入图像序列的深度信息选择空间,然后联合尺度内序列关联与尺度间信息约束进行代价聚合,通过这种扩张-聚合模式实现了场景深度表征信息的倍增与跨尺度和跨序列表征信息的有效融合. 作为一种通用框架,MSCAS框架可实现已有模型设计类方法和深度学习类方法的嵌入进而实现性能提升. 实验结果表明:MSCAS框架在嵌入模型设计类SFF方法后4组数据集中的均方根误差RMSE(root mean squared error)平均下降14.91%,结构相似性SSIM(structural similarity index measure)平均提升56.69%,嵌入深度学习类SFF方法后4组数据集中的RMSE平均下降1.55%,SSIM平均提升1.61%. 验证了MSCAS框架的有效性和通用性.

       

      Abstract: 3D shape reconstruction aims to recover the 3D structure information of the scene by using image sequences with different focus levels. Most of the existing 3D shape reconstruction methods evaluate the focus level of the image sequence from a single scale, and guide the reconstruction process by introducing regularization or post-processing methods. Due to the limitation of the selection space of depth information, the reconstruction results often cannot converge effectively. To address this issue, this paper proposes a multi-scale cost aggregation framework for shape from focus, MSCAS. Firstly, non-downsampling multi-scale transformation is introduced to increase the depth information selection space of the input image sequence, and then the cost aggregation is performed by combining the intra-scale sequence correlation and the inter-scale information constraint. Through this expansion-aggregation mode, the doubling of scene depth representation information and the effective fusion of cross-scale and cross-sequence representation information are realized. As a general framework, the MSCAS framework can embed existing model design methods and deep learning methods to achieve performance improvement. The experimental results show that the MSCAS framework in this paper reduces the root mean square error (RMSE) on average by 14.91% and improves the structural similarity (SSIM) by 56.69% in the four datasets after embedding the model design class SFF method. After embedding the deep learning class SFF method, the RMSE in the four datasets decreases by an average of 1.55% and the SSIM increases by an average of 1.61%. These results verify the effectiveness of the MSCAS framework.

       

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