Although the streaked optical pyrometer (SOP) system has been widely adopted in shock temperature measurements, its reliability has always been of concern. Here, two calibrated Planckian radiators with different color temperatures were used to calibrate and verify the SOP system by comparing the two calibration standards using both multi-channel and single-channel methods. A high-color-temperature standard lamp and a multi-channel filter were specifically designed for the measurement system. To verify the reliability of the SOP system, the relative deviation between the measured data and the standard value of less than 5% was calibrated out, which demonstrates the reliability of the SOP system. Furthermore, a method to analyze the uncertainty and sensitivity of the SOP system is proposed. A series of laser-induced shock experiments were conducted at the ‘Shenguang-II’ laser facility to verify the reliability of the SOP system for temperature measurements at tens of thousands of kelvin. The measured temperature of the quartz in our experiments agreed fairly well with previous works, which serves as evidence for the reliability of the SOP system.

Temperature is one of the most important parameters for characterizing the thermodynamic state of matter in extreme conditions. However, there is as of yet no universal and accurate way to measure the temperature associated with a shock wave propagating in an opaque material, let alone an inversion method for determining how this temperature evolves. Based on the current strong generalization and learning abilities of artificial neural networks, this paper proposes using an artificial neural network to determine (i) how the shock-wave temperature in a material evolves and (ii) the surface temperature of the interface between the material and vacuum when a shock wave propagates through the material. Data generated using a one-dimensional numerical hydrodynamic simulation are used to train the artificial neural network by applying backpropagation and optimization to many datasets. Once the artificial neural network is trained sufficiently, it becomes an excellent approximator that can estimate the shock-wave temperature from a given streaked-optical-pyrometer image and other known information from the experiment. The paper ends with various possible extensions to the present research.

The lack of three-dimensional (3D) content is one of the challenges that have been faced by holographic 3D display. Here, we proposed a real 3D scene acquisition and 3D holographic reconstruction system based on ultrafast optical axial scanning. An electrically tunable lens (ETL) was used for high-speed focus shift (up to 2.5 ms). A CCD camera was synchronized with the ETL to acquire multi-focused image sequence of real scene. Then, the focusing area of each multi-focused image was extracted by using Tenengrad operator, and the 3D image were obtained. Finally, 3D holographic reconstruction visible to the naked eye can be achieved by the layer-based diffraction algorithm. The feasibility and effectiveness of the proposed method have been demonstrated by simulation and experiment, and the experimental results agree well with the simulation results. This method will further expand the application of holographic 3D display in the field of education, advertising, entertainment, and other fields.

Over the past few years, 3D display technology has improved so much that it is being used in many fields, such as education, medicine and the military. 3D holographic display is seen as the ultimate solution to 3D display, but one of the problems is that there is not enough 3D content available. Conventional methods to obtain 3D content from real scenes using lightfield cameras or RGB-D cameras are complicated. Here, we proposed a 3D scene acquisition and reconstruction system based on optical axial scanning. First an electrically tunable lens (ETL) was used for high-speed focus shift (up to 2.5 ms). A CCD camera is synchronized with the ETL to acquire multi-focused image sequence of real scene. Then, The Tenengrad operator was used to obtain the focusing area of each multi-focused image, and the 3D image were obtained. Finally, the Computer-Generated Hologram (CGH) can be obtained by the layer-based diffraction algorithm. The CGH was loaded onto the space light modulator (SLM) to reconstruct the 3D holographic image. The experimental results verify the feasibility of the system. This method will expand the application of 3D holographic display in the field of education, advertising, entertainment, and other fields.

In the traditional Fourier single-pixel imaging (FSPI), compressed sampling is often used to improve the acquisition speed. However, the reconstructed image after compressed sampling often has a lower resolution and the quality is difficult to meet the imaging requirements of practical applications. To address this issue, we proposed a novel imaging method that combines deep learning and single-pixel imaging, which can reconstruct high-resolution images with only a small-scale sampling. In the training phase of the network, we attempted to incorporate the physical process of FSPI into the training process. To achieve this objective, a large number of natural images were selected to simulate Fourier single-pixel compressed sampling and reconstruction. The compressed reconstructed samples were subsequently employed for network training. In the testing phase of the network, the compressed reconstruction samples of the test dataset were input into the network for optimization. The experimental results showed that compared with traditional compressed reconstruction methods, this method effectively improved the quality of reconstructed images.