Compressed ultrafast photography (CUP) is the fastest single-shot passive ultrafast optical imaging technique, which has shown to be a powerful tool in recording self-luminous or non-repeatable ultrafast phenomena. However, the low fidelity of image reconstruction based on the conventional augmented-Lagrangian (AL) and two-step iterative shrinkage/thresholding (TwIST) algorithms greatly prevents practical applications of CUP, especially for those ultrafast phenomena that need high spatial resolution. Here, we develop a novel AL and deep-learning (DL) hybrid (i.e., AL + DL ) algorithm to realize high-fidelity image reconstruction for CUP. The AL + DL algorithm not only optimizes the sparse domain and relevant iteration parameters via learning the dataset but also simplifies the mathematical architecture, so it greatly improves the image reconstruction accuracy. Our theoretical simulation and experimental results validate the superior performance of the AL + DL algorithm in image fidelity over conventional AL and TwIST algorithms, where the peak signal-to-noise ratio and structural similarity index can be increased at least by 4 dB (9 dB) and 0.1 (0.05) for a complex (simple) dynamic scene, respectively. This study can promote the applications of CUP in related fields, and it will also enable a new strategy for recovering high-dimensional signals from low-dimensional detection.

Being capable of passively capturing transient scenes occurring in picoseconds and even shorter time with an extremely large sequence depth in a snapshot, compressed ultrafast photography (CUP) has aroused tremendous attention in ultrafast optical imaging. However, the high compression ratio induced by large sequence depth brings the problem of low image quality in image reconstruction, preventing CUP from observing transient scenes with fine spatial information. To overcome these restrictions, we propose an efficient image reconstruction algorithm with multi-scale (MS) weighted denoising based on the plug-and-play (PnP) based alternating direction method of multipliers (ADMM) framework for multi-channel coupled CUP (MC-CUP), named the MCMS-PnP algorithm. By removing non-Gaussian distributed noise using weighted MS denoising during each iteration of the ADMM, and adaptively adjusting the weights via sufficiently exploiting the coupling information among different acquisition channels collected by MC-CUP, a synergistic combination of hardware and algorithm can be realized to significantly improve the quality of image reconstruction. Both simulation and experimental results demonstrate that the proposed adaptive MCMS-PnP algorithm can effectively improve the accuracy and quality of reconstructed images in MC-CUP, and extend the detectable range of CUP to transient scenes with fine structures.

Optical-resolution photoacoustic microscopy (OR-PAM) is a powerful imaging technique that visualizes microvascular and tissue structures with high spatial resolution, offering valuable insights into physiological and pathological processes. However, strong optical scattering in biological tissues fundamentally limits its imaging depth. Recent studies have suggested that tartrazine, a food-grade water-soluble dye, may serve as an effective optical clearing agent (OCA), yet its strong optical absorption near 532 nm raises concerns about compatibility with green-light-based OR-PAM systems. In this study, we demonstrate that by carefully controlling the concentration of tartrazine, it can effectively function as an OCA for OR-PAM at 532 nm without compromising signal quality. In vivo experiments on mouse ear and abdominal tissue showed that tartrazine significantly enhanced microvascular visibility across all tested concentrations, with the 15% weight of solute per weight of solution providing optimal clearing performance. Additionally, we evaluated 4-aminoantipyrine as another dye-based OCA, which also improved image clarity, albeit to a lesser extent. These findings highlight the promise of absorbing dye-based OCAs like tartrazine in enhancing in vivo OR-PAM by mitigating light scattering, potentially enabling deeper and clearer photoacoustic imaging in biomedical applications.

Structured illumination microscopy (SIM), as a flexible tool, has been widely applied to observing subcellular dynamics in live cells. It is noted, however, that SIM still encounters a problem with theoretical resolution limitation being only twice over wide-field microscopy, where imaging of finer biological structures and dynamics are significantly constrained. To surpass the resolution limitation of SIM, we developed an image postprocessing method to further improve the lateral resolution of SIM by an untrained neural network, i.e., deep resolution-enhanced SIM (DRE-SIM). DRE-SIM can further extend the spatial frequency components of SIM by employing the implicit priors based on the neural network without training datasets. The further super-resolution capability of DRE-SIM is verified by theoretical simulations as well as experimental measurements. Our experimental results show that DRE-SIM can achieve the resolution enhancement by a factor of about 1.4 compared with conventional SIM. Given the advantages of improving the lateral resolution while keeping the imaging speed, DRE-SIM will have a wide range of applications in biomedical imaging, especially when high-speed imaging mechanisms are integrated into the conventional SIM system.

The ability to tune color output of nanomaterials is very important for their applications in laser, optoelectronic device, color display and multiplexed biolabeling. Here we first propose a femtosecond pulse shaping technique to realize the up-conversion fluorescence tuning in lanthanide-doped nanocrystals dispersed in the glass. The multiple subpulse formation by a square phase modulation can create different excitation pathways for various up-conversion fluorescence generations. By properly controlling these excitation pathways, the multicolor up-conversion fluorescence can be finely tuned. This color tuning by the femtosecond pulse shaping technique is realized in single material by single-color laser field, which is highly desirable for further applications of the lanthanide-doped nanocrystals. This femtosecond pulse shaping technique opens an opportunity to tune the color output in the lanthanide-doped nanocrystals, which may bring a new revolution in the control of luminescence properties of nanomaterials.