Speckle correlation imaging (SCI) has found tremendous versatility compared with other scattering imaging approaches due to its single-shot data acquisition strategy, relatively simple optical setup, and high-fidelity reconstruction performance. However, this simplicity requires SCI experiments to be performed strictly in a darkroom condition. As background noise increases, the speckle contrast rapidly decreases, making precise interpretation of the data extremely difficult. Here, we demonstrate a method by refining the speckle in the autocorrelation domain to achieve high-performance single-shot imaging. Experiment results prove that our method is adapted to estimate objects in a low signal-to-background ratio (SBR) circumstance even if the SBR is about − 23 d B . Laboratory and outdoor SCI experiments are performed.

The minimalist optical system has a simple structure, small size, and lightweight, but the low optical complexity will produce optical aberration. Addressing the significant aberration degradation in minimalist systems, we propose a high-quality computational optical framework. This framework integrates a global point spread function (PSF) change imaging model with a transformer-based U-Net deep learning algorithm to achieve high-quality imaging in minimalist systems. Additionally, we introduce an imaging performance evaluation method based on the modulation transfer degree of resolution (MTR). We addressed severe chromatic and spherical aberrations in single-lens systems, a typical example of minimalist optical systems, by simulating the degradation process and reconstructing the imaging effects. This approach demonstrated significant improvements, thus validating the feasibility of our method. Specifically, our technique calculated the MTR values in real images captured with the GCL010109 single lens at 0.8085, and with the GCL010110 single lens at 0.8055. Our method enhanced the imaging performance of minimalist systems by 4 times, upgrading minimalist system capabilities from poor to good lens grade. This work can provide reference for wavefront coding, matelens, diffraction optical systems, and other computational imaging work. It can also promote the application of miniaturization of medical, aerospace, and head-mounted optical systems.

The premixed ethylene and oxygen flame that is burning in a narrow channel is investigated with digital holographic interferometry (DHI). Combustion in either a narrow tube or channel is quite different. This is caused by the significant effects of the boundary layer. The flame’s acceleration rate will be enhanced as the tube diameter decreases. Usually, flame and shock wave propagation, which occurs during the premixed ethylene/oxygen flame combustion in the measurement area, is less than few milliseconds, so that general camera can rarely capture this fast event. This paper demonstrates that, by introducing the high-speed camera to DHI, the propagation of weak compression wave, flame, and shock wave generated in the narrow channel is successfully measured with a temporal resolution of 10 μs. The ultrafast processes of the flame front changing, as well as the shock wave coupling and separating, are clearly shown from the reconstructed phase distributions of the recorded holograms; corresponding density variations are simultaneously calculated. The results could provide references for the micro-scale propulsion and power devices design and use, and this proposed configuration can also easily adapt to other kinds of ultrafast processes in fluids.

With the development of computational imaging, the integration of optical system design and digital algorithms has made more imaging tasks easier to perform. Wavefront coding (WFC) is a typical computational imaging technique that is used to address the constraints of optical aperture and depth of field. In this paper, we demonstrated a low-cost and simple optical system based on WFC and deep learning. We constructed an optimized encoding method for the phase plate under the framework of deep learning, which reduces the requirement for aberration correction in the full field of view. Optical coding was achieved with just a double-bonded lens and a simple cubic phase mask, and digital decoding used the deep residual UNet++ network framework. The final image obtained has good resolution, whereas the depth of field of the system expanded by a factor of 13, which is of great significance for the high-precision inspection and attaching of small parts of machine vision.