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.

对溶液化发光层成膜参数及电子传输层浓度进行调控，优化发光层成膜效果及器件发光性能，同时使用导电聚合物聚（3，4-乙烯二氧噻吩）-聚苯乙烯磺酸（PEDOT：PSS）作为透明阳极，刮涂导电银浆作为阴极，通过全溶液法制备了高效率的OLED。研究发现，发光层成膜参数的调整有效改善了其成膜效果。且适当的电子传输层材料浓度可以改善器件的载流子注入平衡，有效降低阴极的功函数，提高器件的发光性能；酸后处理的PEDOT：PSS薄膜导电性大大提升，在可见光范围的透过率与ITO相当。全溶液制备的发光器件最大电流效率为1.441 cd/A，与以ITO为电极的器件相比，增加了近50倍。

In-situ laser-induced surface damage inspection plays a key role in protecting the large aperture optics in an inertial confinement fusion (ICF) high-power laser facility. In order to improve the initial damage detection capabilities, an in-situ inspection method based on image super-resolution and adaptive segmentation method is presented. Through transfer learning and integration of various attention mechanisms, the super-resolution reconstruction of darkfield images with less texture information is effectively realized, and, on the basis of image super-resolution, an adaptive image segmentation method is designed, which effectively adapts to the damage detection problems under conditions of uneven illumination and weak signal. An online experiment was carried out by using edge illumination and the telescope optical imaging system, and the validity of the method was proved by the experimental results.

为了实现快速准确并可回收再利用地鉴别真血和假血, 采用光声光谱技术构建了一套血液光声检测系统并获取血样的光声信号。 选取三种动物真血(马血、 牛血和兔血)和两种假血(道具假血和红墨汁)共125组血样作为实验样本。 获取了700~1 064 nm波段内所有样本的光声信号和光声峰峰值谱。 实验表明, 真血和假血的光声信号幅度、 轮廓、 峰值时间点和光声峰峰值均存在差异。 为了实现高准确度的真假血液分类识别, 采用了遗传优化的小波神经网络(WNN-GA)算法, 对全波段100组样本进行训练, 并构建了类Morlet小波基函数, 然后对25组测试血样进行分类识别。 利用遗传算法对WNN网络的权值、 阈值和小波基函数平移、 伸缩因子进行了优化, 同时通过调节两个学习率因子, 将真假血液的分类识别率提高了24%。 采用主成分分析(PCA)对全波段血样光声峰峰值进行特征提取, 再利用WNN-GA算法进行训练和分类识别。 结果表明, 在主成分个数为6时, PCA-WNN-GA融合算法可以使真假血液的分类识别率提高到100%。 与另外6种分类识别算法相对比, 该融合算法的识别准确率明显占优。 光声光谱技术联合PCA-WNN-GA算法, 可以准确地实现真假血液的分类鉴别。

Photoacoustic microscopy (PAM) has quickly developed into a noninvasive biomedical imaging technique to achieve detection, diagnosis, and monitoring. Compared with Q-switched neodymium-doped yttrium aluminum garnet or optical parametric oscillator lasers, a low-cost and small-size laser diode (LD) used as an alternative light source is conducive to achieving the miniaturization and integration for preclinical transformation. However, the LD’s low peak output power needs the high numerical aperture objective to attain tight focus, which limits the working distance (WD) of the system in only 2–3 mm, resulting in not achieving the backward coaxial confocal mode. Here, we present a compact visible LD-based PAM system with a reflective objective to achieve a 22 mm long WD and a 10 µm lateral resolution. Different depth subcutaneous microvascular networks in label-free mouse ears have successfully reappeared in vivo with a signal-to-noise ratio up to 14 dB by a confocal alignment. It will be a promising tool to develop into a handy tool for subcutaneous blood vessel imaging.

红外弱小目标的目标像素少，目标对比度低，成像帧率高，图像数据量大，检测实时性强。针对红外弱小目标检测算法适合于GPU并行计算的特点，对其在嵌入式GPU平台Jetson TX2上进行了并行优化实现。在检测算法设计、内存访问、调试优化3个方面进行了优化设计。实验结果表明，对640×480像素分辨率的红外视频，并行优化后的目标检测算法能够在10 ms内完成计算，满足实时处理需求。