Imaging Transparent Objects in a Turbid Medium Using a Femtosecond Optical Kerr Gate

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.

Transparent Object Reconstruction Based on Compressive Sensing and Super-Resolution Convolutional Neural Network

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.

Reflection separation technology based on polarization characteristics

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.