The laser-induced damage detection images used in high-power laser facilities have a dark background, few textures with sparse and small-sized damage sites, and slight degradation caused by slight defocus and optical diffraction, which make the image superresolution (SR) reconstruction challenging. We propose a non-blind SR reconstruction method by using an exquisite mixing of high-, intermediate-, and low-frequency information at each stage of pixel reconstruction based on UNet. We simplify the channel attention mechanism and activation function to focus on the useful channels and keep the global information in the features. We pay more attention on the damage area in the loss function of our end-to-end deep neural network. For constructing a high-low resolution image pairs data set, we precisely measure the point spread function (PSF) of a low-resolution imaging system by using a Bernoulli calibration pattern; the influence of different distance and lateral position on PSFs is also considered. A high-resolution camera is used to acquire the ground-truth images, which is used to create a low-resolution image pairs data set by convolving with the measured PSFs. Trained on the data set, our network has achieved better results, which proves the effectiveness of our method.

Wide-field linear structured illumination microscopy (LSIM) extends resolution beyond the diffraction limit by moving unresolvable high-frequency information into the passband of the microscopy in the form of moiré fringes. However, due to the diffraction limit, the spatial frequency of the structured illumination pattern cannot be larger than the microscopy cutoff frequency, which results in a twofold resolution improvement over wide-field microscopes. This Letter presents a novel approach in point-scanning LSIM, aimed at achieving higher-resolution improvement by combining stimulated emission depletion (STED) with point-scanning structured illumination microscopy (psSIM) (STED-psSIM). The according structured illumination pattern whose frequency exceeds the microscopy cutoff frequency is produced by scanning the focus of the sinusoidally modulated excitation beam of STED microscopy. The experimental results showed a 1.58-fold resolution improvement over conventional STED microscopy with the same depletion laser power.

The ongoing quest for higher data storage density has led to a plethora of innovations in the field of optical data storage. This review paper provides a comprehensive overview of recent advancements in next-generation optical data storage, offering insights into various technological roadmaps. We pay particular attention to multidimensional and superresolution approaches, each of which uniquely addresses the challenge of dense storage. The multidimensional approach exploits multiple parameters of light, allowing for the storage of multiple bits of information within a single voxel while still adhering to diffraction limitation. Alternatively, superresolution approaches leverage the photoexcitation and photoinhibition properties of materials to create diffraction-unlimited data voxels. We conclude by summarizing the immense opportunities these approaches present, while also outlining the formidable challenges they face in the transition to industrial applications.

光学干涉仪是现代精密测量技术的核心支撑，但其分辨率受到光源波长的限制，无法通过无限减小波长提高分辨率，而“相位超分辨”即是指设法解决光源波长限制的技术手段。目前“相位超分辨”研究主要通过调控 $ N $光子纠缠态的途径实现，但是由于 $ N $光子纠缠态制备与调控的极高难度和符合计数的极低效率使得该途径无法用于实际测量。针对这一瓶颈，笔者联合团队利用轨道角动量（OAM）相干态在光学超晶格中的级**量上转换过程高效构造、提取多光子复振幅信号。实现了 $ N=12 $倍的相位超分辨干涉信号的实时测量，为发展可实际应用的高倍率相位超分辨干涉测量技术提供了一条全新的物理途径。

Stimulated Raman scattering (SRS) microscopy has shown superior chemical resolution due to the much narrower vibrational spectral bandwidth than its fluorescence counterpart. However, breaking the diffraction-limited spatial resolution of SRS imaging is much more challenging because of the intrinsically weak scattering cross section and inert/stable nature of molecular bond vibrations. We report superresolution SRS (SR-SRS) nanoscopy based on reversible-switchable vibrational photochromic probes integrated with point spread function engineering strategy. By introducing a Gaussian-shaped ultraviolet excitation beam and a donut-shaped visible depletion beam in addition to the pump and Stokes beams, SR-SRS could reach sub-100 nm resolution on photoswitchable nanoparticles (NPs). Furthermore, NP-treated live cell imaging was demonstrated with resolution improvement by a factor of ∼4. Our proof-of-principle work provides the potential for SR vibrational imaging to assist research on complex biological systems.

Terahertz (THz) technology offers novel opportunities in biology and medicine, thanks to the unique features of THz-wave interactions with tissues and cells. Among them, we particularly notice strong sensitivity of THz waves to the tissue water, as a medium for biochemical reactions and a main endogenous marker for THz spectroscopy and imaging. Tissues of the brain have an exceptionally high content of water. This factor, along with the features of the structural organization and biochemistry of neuronal and glial tissues, makes the brain an exciting subject to study in the THz range. In this paper, progress and prospects of THz technology in neurodiagnostics are overviewed, including diagnosis of neurodegenerative disease, myelin deficit, tumors of the central nervous system (with an emphasis on brain gliomas), and traumatic brain injuries. Fundamental and applied challenges in study of the THz-wave – brain tissue interactions and development of the THz biomedical tools and systems for neurodiagnostics are discussed.

Structured illumination microscopy (SIM) has been widely applied in the superresolution imaging of subcellular dynamics in live cells. Higher spatial resolution is expected for the observation of finer structures. However, further increasing spatial resolution in SIM under the condition of strong background and noise levels remains challenging. Here, we report a method to achieve deep resolution enhancement of SIM by combining an untrained neural network with an alternating direction method of multipliers (ADMM) framework, i.e., ADMM-DRE-SIM. By exploiting the implicit image priors in the neural network and the Hessian prior in the ADMM framework associated with the optical transfer model of SIM, ADMM-DRE-SIM can further realize the spatial frequency extension without the requirement of training datasets. Moreover, an image degradation model containing the convolution with equivalent point spread function of SIM and additional background map is utilized to suppress the strong background while keeping the structure fidelity. Experimental results by imaging tubulins and actins show that ADMM-DRE-SIM can obtain the resolution enhancement by a factor of ∼1.6 compared to conventional SIM, evidencing the promising applications of ADMM-DRE-SIM in superresolution biomedical imaging.