Coded x-ray diffraction imaging (CXRDI) is an emerging computational imaging approach that aims to solve the phase retrieval problem in x-ray crystallography based on the intensity measurements of encoded diffraction patterns. Boolean coding masks (BCMs) with complementary structures have been used to modulate the diffraction pattern in CXRDI. However, the optimal spatial distribution of BCMs still remains an open problem to be studied in depth. Based on the spectral initialization criterion, we provide a theoretical proof for the premise that the optimal complementary BCMs should obey the blue noise distribution in the sense of mathematical expectation. In addition, the benefits of the blue noise coding strategy are assessed by a set of simulations, where better reconstruction quality is observed compared to the random BCMs and other complementary BCMs.We experimentally demonstrate a camera whose primary optic is a cannula/needle (diameter=0.22mm and length=12.5mm) that acts as a light pipe transporting light intensity from an object plane (35 cm away) to its opposite end. Deep neural networks (DNNs) are used to reconstruct color and grayscale images with a field of view of 18° and angular resolution of ∼0.4∘. We showed a large effective demagnification of 127×. Most interestingly, we showed that such a camera could achieve close to diffraction-limited performance with an effective numerical aperture of 0.045, depth of focus ∼16µm, and resolution close to the sensor pixel size (3.2 µm). When trained on images with depth information, the DNN can create depth maps. https://www.selleckchem.com/products/cddo-im.html Finally, we show DNN-based classification of the EMNIST dataset before and after image reconstructions. The former could be useful for imaging with enhanced privacy.A novel, to the best of our knowledge, depth-sensing technology that enables a shallow depth of field was developed by adding a diffuser to the rear end of a mechanical control lens that can capture 2D images. The sensor in the optical depth-sensing system obtains the function curve between the motor step and the focus distance through calibration and imports the measured values into the control program's database. The optical depth-sensing system scans the visible range of an interval, and the Laplacian equation can be applied to confirm whether the interval was in focus by judging the sharpness of the contour of the objects captured in the interval and to define the outline of the objects. Then, the depth information can be obtained by calculating the focus distance based on the motor step during scanning. Finally, the focus images of individual objects are used to calculate the image contours in the depth direction. The focus images of each object are combined to reconstruct a 2.5D model within the sensing range. The optical depth-sensing system is not affected by sunlight or the material of the measured object. Furthermore, the system can be used to obtain color images by using a modified lens. The optical path is simple and does not require complex calculations. Therefore, the proposed system is not easily affected by the environment and exhibits high resolution and calculation speed.The deep learning wavefront sensor (DLWFS) allows the direct estimate of Zernike coefficients of aberrated wavefronts from intensity images. The main drawback of this approach is related to the use of massive convolutional neural networks (CNNs) that are lengthy to train or estimate. In this paper, we explore several options to reduce both the training and estimation time. First, we develop a CNN that can be rapidly trained without compromising accuracy. Second, we explore the effects given smaller input image sizes and different amounts of Zernike modes to be estimated. Our simulation results demonstrate that the proposed network using images of either 8×8, 16×16, or 32×32 will dramatically reduce training time and even boost the estimation accuracy of Zernike coefficients. From our experimental results, we can confirm that a 16×16 DLWFS can be quickly trained and is able to estimate the first 12 Zernike coefficients-skipping piston, tip, and tilt-without sacrificing accuracy and significantly speeding up the prediction time to facilitate low-cost, real-time adaptive optics systems.Recently, an optical scanning holographic system with a polarization directed flat lens was proposed to realize coaxial scanning holography (CSH). The advantage of CSH is its small form factor and the stability. However, the diffraction efficiency of the polarization directed flat lens cannot be 100%, and thus there is always zeroth order light in the scanning beam. The imperfect diffraction property of the polarization directed flat lens results in an incomplete scanning Fresnel zone plate. Consequently, the reconstructed image is blurred and noisy. In this paper, we compared different methods, including the back propagation, the phase correlation, and inverse filtering, for the hologram reconstruction. It is demonstrated that inverse filtering is the only method that can retrieve the high-frequency component of the hologram. However, additional noise also arises with the use of inverse filtering. Therefore, the imaging performance of CSH by using a polarization directed flat lens is inherently worse than that of conventional OSH.The proposed total internal reflection (TIR)-based technique can be used for measuring the refractive index of lenses. Distribution of the phase difference between the s- and p-polarization states of the reflected light induced by TIR can be obtained by a polarization camera. The refractive index of the lens can be determined from the detected maximum phase difference, with the specific measurement equation. Only the maximum phase difference needs to be measured. Information about the incident angle, thickness of the lens, and the matching liquid is not needed. The experimental results demonstrate that the resolution of the system can reach 4.8×10-4RIU.Optical imaging is a powerful tool for nondestructive inspection, with high spatial resolution and low invasiveness. As light-material interactions vary a great deal depending on the wavelength, it is difficult to select the best imaging wavelength without prior knowledge of the optical properties of the material. To overcome this difficulty, we constructed a hybrid optical imaging system using three different wavelengths near-infrared (NIR), mid-infrared (MIR), and terahertz (THz) regions. The same imaging optics were integrated with different light sources and detectors. Depending on the light-material interaction and detection sensitivity, NIR and THz imaging indicated some potential for nondestructive inspection, but MIR imaging showed difficulty. A combination of NIR and THz imaging will be a powerful tool for optical nondestructive inspection.