为了全面且针对性地研究水下湍流成像的退化因素，同时优化相应图像恢复算法，搭建了一个可控湍流条件和重复使用的水下成像实验系统，利用循环水泵控制实验水箱中湍流的强度，气泡发生器制造微气泡，图像传感器获取不同条件下的正弦条纹目标板的成像结果。研究了流速场、程辐射和流体介质对水下成像的影响，结合图像复原和超分辨率重建技术，比较了基于三种退化因素的调制传递函数(MTF)的差异和适用性。结果表明，湍流流速场在低空间频率段造成MTF 快速下降，程辐射和流体介质则会导致高空间频率的调制对比度减小；在水下湍流退化图像恢复中，湍流流速场的MTF 适合图像复原，程辐射和流体介质的MTF 适合图像重建。

Phase diversity (PD) is a kind of wavefront sensing technology based on image collecting and post-processing. We apply the PD technology to align an off-axis three-mirror reflecting anastigmatic system precisely. It can be concluded that the wavefront error obtained by PD agrees well with the interferometric result. The focused images are also restored according to the testing results of PD, and the qualities of restored images are improved.

Photoacoustic microscopy (PAM) is recognized as a powerful tool for various microcirculation system studies. To improve the spatial resolution for the PAM images, the requirements of the system will always be increased correspondingly. Without additional cost of the system, we address the problem of improving the resolution of PAM images by integrating a deconvolution model with a directional total variation regularization. Additionally, we present a primal-dual-based algorithm to solve the associated optimization problem efficiently. Results from both test images and some PAM images studies validate the effectiveness of the proposed method in enhancing the spatial resolution. We expect the proposed technique to be an alternativeresolution enhancement tool for some important biomedical applications.

A new photoacoustic (PA) signal sampling and image reconstruction method, called compressive sampling PA tomography (CSPAT), is recently proposed to make low sampling rate and high-resolution PA tomography possible. A key problem within the CSPAT framework is the design of optic masks. We propose to use edge expander codes-based masks instead of the conventional random distribution masks, and efficient total variation (TV) regularization-based model to formulate the associated problem. The edge expander codes-based masks, corresponding to non-uniform sampling schemes, are validated by both theoretical analysis and results from computer simulations. The proposed method is expected to enhance the capability of CSPAT for reducing the number of measurements and fast data acquisition.

Wavefront coding (WFC) is kind of computational imaging technique that controls misfocus and misfocus-related aberrations of optical systems by appending a specially designed phase distribution to the pupil function. This technology has been applied in many fields to increase the performance or/and reduce the cost of imaging systems. The application of WFC technology on an off-axis three-mirror anastigmatic (TMA) system has been proposed in our previous work. In this letter, we describe the alignment, the imaging experiment and image restoration of an actual TMA system with WFC technology.

The resolution of astronomical imaging from large optical telescopes is usually limited by the blurring effects of refractive index fluctuations in the Earth’s atmosphere. In this letter, we develop a lucky imaging system to restore astronomical images through atmosphere turbulence on large telescope. Our system takes very short exposures, on the order of the atmospheric coherence time. The rapidly changing turbulence leads to a very variable point spread function (PSF), and the variability of the PSF leads to some frames having better quality than the rest. Only the best frames are selected, aligned and co-added to give a final image with much improved angular resolution. Our lucky imaging system is successfully applied to restore the astronomical images taken by a 1.23 m telescope. We get clear images of moon surface, Jupiter, and Saturn, and our system can be demonstrated to greatly improve the imaging resolution through atmospheric turbulence.

The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that, for the CS reconstruction, the desired image should have a sparse representation in a known transform domain. However, the sparsity of photoacoustic signals is destroyed because noises always exist. Therefore, the original sparse signal cannot be effectively recovered using the general reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic images based on a set of noisy CS measurements. Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.