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
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.
