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.

School of Engineering,Monash University Malaysia,Selangor 47500,Malaysia;College of Optoelectronic Engineering,Changchun University of Science and Technology,Jilin 130022,China

[1]Anumol MATHAI;Li MENGDI;Stephen LAU;Ningqun GUO;Xin WANG-.Transparent Object Reconstruction Based on Compressive Sensing and Super-Resolution Convolutional Neural Network)[J].光子传感器（英文版）,2022(04):24-35

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