A femtosecond optical Kerr gate time-gated ballistic imaging method is demonstrated to image a transparent object in a turbid medium. The shape features of the object are obtained by time-resolved selection of the bal...A femtosecond optical Kerr gate time-gated ballistic imaging method is demonstrated to image a transparent object in a turbid medium. The shape features of the object are obtained by time-resolved selection of the ballistic photons with different optical path lengths, the thickness distribution of the object is mapped, and the maximum is less than 3.6%. This time-resolved ballistic imaging has potential applications in studying properties of the liquid core in the near field of the fuel spray.展开更多
Transparent objects are widely used in various fields, leading to increasing demand for methods of measuringthem. However, the measurement of such objects has always been challenging owing to the intricate refractiona...Transparent objects are widely used in various fields, leading to increasing demand for methods of measuringthem. However, the measurement of such objects has always been challenging owing to the intricate refractionand reflection phenomena they exhibit. Given that traditional contact measurement methods can damagetransparent objects, the use of non-destructive measurement techniques, particularly those based on opticalprinciples, is considered preferable. As a result, various non-destructive measurement methods have beendeveloped for transparent objects by leveraging the unique characteristics of light, and a comprehensive review isimperative for exploring these innovative methods and their potential applications. This review accordingly beginsby elucidating the necessity of measuring transparent objects and exploring the concept of transparency. Next, anoverview of various non-destructive optical measurement techniques spanning macro-, micro-, and general-scaleapplications is presented, followed by a discussion of their respective advantages and limitations. Finally, the paperconcludes by outlining future directions for potential advancements in the field. This review is expected to serve asa valuable resource for newcomers in the field of transparent object measurement and assist researchers seeking tointegrate these techniques into interdisciplinary studies.展开更多
In this study,we propose a novel method to reconstruct the 3D shapes of transparent objects using images captured by handheld cameras under natural lighting conditions.It combines the advantages of an explicit mesh an...In this study,we propose a novel method to reconstruct the 3D shapes of transparent objects using images captured by handheld cameras under natural lighting conditions.It combines the advantages of an explicit mesh and multi-layer perceptron(MLP)network as a hybrid representation to simplify the capture settings used in recent studies.After obtaining an initial shape through multi-view silhouettes,we introduced surface-based local MLPs to encode the vertex displacement field(VDF)for reconstructing surface details.The design of local MLPs allowed representation of the VDF in a piecewise manner using two-layer MLP networks to support the optimization algorithm.Defining local MLPs on the surface instead of on the volume also reduced the search space.Such a hybrid representation enabled us to relax the ray–pixel correspondences that represent the light path constraint to our designed ray–cell correspondences,which significantly simplified the implementation of a single-image-based environment-matting algorithm.We evaluated our representation and reconstruction algorithm on several transparent objects based on ground truth models.The experimental results show that our method produces high-quality reconstructions that are superior to those of state-of-the-art methods using a simplified data-acquisition setup.展开更多
Specific to the reflected light problem on the surface of transparent body,the polarization characteristics of the reflection region are analyzed,and a polarization characterization model combining the reflection and ...Specific to the reflected light problem on the surface of transparent body,the polarization characteristics of the reflection region are analyzed,and a polarization characterization model combining the reflection and transmission effects is established.On the basis of the polarization characteristic analysis,the minimum value of normalized cross-correlation(NCC)coefficient between transmission and reflection images is solved through the gradient descent method,and their polarization degrees under the minimum correlation are acquired.According to the distribution relations of the transmitted and reflected lights in perpendicular and parallel directions,reflection separation is realized via the polarized orthogonality difference algorithm based on the degree of reflection polarization and the degree of transmission polarization.展开更多
The detection and reconstruction of transparent objects have remained challenging due to the absence of their features and variations in the local features with variations in illumination.In this paper,both compressiv...The detection and reconstruction of transparent objects have remained challenging due to the absence of their features and variations in the local features with variations in illumination.In this paper,both compressive sensing(CS)and super-resolution convolutional neural network(SRCNN)techniques are combined to capture transparent objects.With the proposed method,the transparent object’s details are extracted accurately using a single pixel detector during the surface reconstruction.The resultant images obtained from the experimental setup are low in quality due to speckles and deformations on the object.However,the implemented SRCNN algorithm has obviated the mentioned drawbacks and reconstructed images visually plausibly.The developed algorithm locates the deformities in the resultant images and improves the image quality.Additionally,the inclusion of compressive sensing minimizes the measurements required for reconstruction,thereby reducing image post-processing and hardware requirements during network training.The result obtained indicates that the visual quality of the reconstructed images has increased from a structural similarity index(SSIM)value of 0.2 to 0.53.In this work,we demonstrate the efficiency of the proposed method in imaging and reconstructing transparent objects with the application of a compressive single pixel imaging technique and improving the image quality to a satisfactory level using the SRCNN algorithm.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos 61427816 and 61690221the Collaborative Innovation Center of Suzhou Nano Science and Technology
文摘A femtosecond optical Kerr gate time-gated ballistic imaging method is demonstrated to image a transparent object in a turbid medium. The shape features of the object are obtained by time-resolved selection of the ballistic photons with different optical path lengths, the thickness distribution of the object is mapped, and the maximum is less than 3.6%. This time-resolved ballistic imaging has potential applications in studying properties of the liquid core in the near field of the fuel spray.
基金supported by the Shanghai Science and Technology Committee Innovation Grant(23ZR1404200)National Natural Science Foundation of China(52075100,52375414).
文摘Transparent objects are widely used in various fields, leading to increasing demand for methods of measuringthem. However, the measurement of such objects has always been challenging owing to the intricate refractionand reflection phenomena they exhibit. Given that traditional contact measurement methods can damagetransparent objects, the use of non-destructive measurement techniques, particularly those based on opticalprinciples, is considered preferable. As a result, various non-destructive measurement methods have beendeveloped for transparent objects by leveraging the unique characteristics of light, and a comprehensive review isimperative for exploring these innovative methods and their potential applications. This review accordingly beginsby elucidating the necessity of measuring transparent objects and exploring the concept of transparency. Next, anoverview of various non-destructive optical measurement techniques spanning macro-, micro-, and general-scaleapplications is presented, followed by a discussion of their respective advantages and limitations. Finally, the paperconcludes by outlining future directions for potential advancements in the field. This review is expected to serve asa valuable resource for newcomers in the field of transparent object measurement and assist researchers seeking tointegrate these techniques into interdisciplinary studies.
基金supported by“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01181)supported by National Natural Science Foundation of China(No.62302134)+1 种基金Zhejiang Provincial Natural Science Foundation(No.LQ24F020031)supported by Information Technology Center and State Key Lab of CAD&CG,Zhejiang University.
文摘In this study,we propose a novel method to reconstruct the 3D shapes of transparent objects using images captured by handheld cameras under natural lighting conditions.It combines the advantages of an explicit mesh and multi-layer perceptron(MLP)network as a hybrid representation to simplify the capture settings used in recent studies.After obtaining an initial shape through multi-view silhouettes,we introduced surface-based local MLPs to encode the vertex displacement field(VDF)for reconstructing surface details.The design of local MLPs allowed representation of the VDF in a piecewise manner using two-layer MLP networks to support the optimization algorithm.Defining local MLPs on the surface instead of on the volume also reduced the search space.Such a hybrid representation enabled us to relax the ray–pixel correspondences that represent the light path constraint to our designed ray–cell correspondences,which significantly simplified the implementation of a single-image-based environment-matting algorithm.We evaluated our representation and reconstruction algorithm on several transparent objects based on ground truth models.The experimental results show that our method produces high-quality reconstructions that are superior to those of state-of-the-art methods using a simplified data-acquisition setup.
基金supported by the National Natural Science Foundation of China(62075239,61302145).
文摘Specific to the reflected light problem on the surface of transparent body,the polarization characteristics of the reflection region are analyzed,and a polarization characterization model combining the reflection and transmission effects is established.On the basis of the polarization characteristic analysis,the minimum value of normalized cross-correlation(NCC)coefficient between transmission and reflection images is solved through the gradient descent method,and their polarization degrees under the minimum correlation are acquired.According to the distribution relations of the transmitted and reflected lights in perpendicular and parallel directions,reflection separation is realized via the polarized orthogonality difference algorithm based on the degree of reflection polarization and the degree of transmission polarization.
基金This research was funded by the Ministry of Higher Education,Malaysia(Grant No.Grant FRGS/1/2020/ICT02/MUSM/02/1).
文摘The detection and reconstruction of transparent objects have remained challenging due to the absence of their features and variations in the local features with variations in illumination.In this paper,both compressive sensing(CS)and super-resolution convolutional neural network(SRCNN)techniques are combined to capture transparent objects.With the proposed method,the transparent object’s details are extracted accurately using a single pixel detector during the surface reconstruction.The resultant images obtained from the experimental setup are low in quality due to speckles and deformations on the object.However,the implemented SRCNN algorithm has obviated the mentioned drawbacks and reconstructed images visually plausibly.The developed algorithm locates the deformities in the resultant images and improves the image quality.Additionally,the inclusion of compressive sensing minimizes the measurements required for reconstruction,thereby reducing image post-processing and hardware requirements during network training.The result obtained indicates that the visual quality of the reconstructed images has increased from a structural similarity index(SSIM)value of 0.2 to 0.53.In this work,we demonstrate the efficiency of the proposed method in imaging and reconstructing transparent objects with the application of a compressive single pixel imaging technique and improving the image quality to a satisfactory level using the SRCNN algorithm.
