The vacuum degree is the key parameter reflecting the quality and performance of vacuum glass. This investigation proposed a novel method, based on digital holography, to detect the vacuum degree of vacuum glass. The detection system was composed of an optical pressure sensor, a Mach–Zehnder interferometer and software. The results showed that the deformation of monocrystalline silicon film in an optical pressure sensor could respond to the attenuation of the vacuum degree of vacuum glass. Using 239 groups of experimental data, pressure differences were shown to have a good linear relationship with the optical pressure sensor’s deformations; pressure differences were linearly fitted to obtain the numerical relationship between pressure difference and deformation and to calculate the vacuum degree of the vacuum glass. Measuring the vacuum degree of vacuum glass under three different conditions proved that the digital holographic detection system could measure the vacuum degree of vacuum glass quickly and accurately. The optical pressure sensor’s deformation measuring range was less than 4.5 μm, the measuring range of the corresponding pressure difference was less than 2600 pa, and the measuring accuracy’s order of magnitude was 10 pa. This method has potential market applications.

In this paper, a layered method based on focal depth is proposed for the fast generation of computational holograms. The method layers objects with focal depth as spacing and approximates triangles on the object as projections on the layers based on the physical properties of the focal depth to simplify the computation. Finally, the diffraction distributions of all layers are calculated via angular spectral diffraction and superimposed to obtain the hologram. The proposed method has been proven to be about 20 times faster on a CPU than the analytical polygon-based method. A hologram containing tens of thousands of triangles can be computed on a GPU in a fraction of a second. In addition, this method makes it easy to attach complex textures, which is difficult with polygon-based analysis methods. Finally, holograms of objects with complex textures were generated, and the three-dimensionality of these holograms was confirmed by numerical and optical reconstruction.

In this paper, we propose a computer-generated hologram (CGH) iterative generation model based with a recurrent neural network architecture, which combines an improved residual complementary neural network with the traditional Gerchberg-Saxton (GS) algorithm. This model is referred to as GS-RNN and is designed to generate high-quality phase-only holograms (POH) with a reduced number of iterations. By incorporating trainable parameters from the neural network into the GS algorithm, GS-RNN enhances the model’s performance through learning from sample data. After learning from the samples, the iterative computation efficiency of GS-RNN achieved three times that of the traditional GS algorithm. Even when the physical parameters for generating POH are changed, GS-RNN can maintain its outstanding performance without the need for retraining. Furthermore, the optical experimental results are consistent with the simulation results, further validating the effectiveness and success of the proposed GS-RNN model.

Since the advent of lasers, coherent optical imaging has found significant applications in optical precision measurement. Among these, digital holography is a key research area. In detection studies, accurately obtaining the amplitude and phase of the object being measured through digital holographic image reconstruction is a critical research task. However, current coherent optical imaging formulas can only calculate the amplitude distribution of the image light field when the object size is smaller than one-quarter of the diameter of the optical system’s entrance pupil, making it difficult to meet the needs of applied research. This paper derives formulas that are not restricted by the size of the incident pupil and can calculate the amplitude and phase distributions of the image light field. Based on the mathematical analysis of the formulas, this paper introduces a technique for obtaining the amplitude and phase distribution of detected objects and provides experimental evidence.

Weld stability is directly related to the safety and reliability of engineering, and continuous improvement of its detection technology has great research significance. This paper presents a novel method for weld stability detection based on color digital holography. A color digital holography optical path was designed to capture the holograms of welds under varying loads. Several common denoising algorithms were used to deal with speckle noise in the wrapped phase, among which the 4-f optical simulation integrated cycle-consistent generative adversarial network (4f-CycleGAN) denoising algorithm based on deep learning was more suitable for the color digital holographic detection system. The three-dimensional deformation fields of three samples (lap-jointed, butt-jointed, and defective butt-jointed aluminum alloy plates) under different loads were calculated. The center profile of the deformation field in the direction of load and holographic reconstruction images are used to determine the position of the weld. The coefficient of variation near the weld was used to evaluate the weld stability. The coefficient of variation for lap-jointed, butt-jointed and defective butt-jointed plates are 0.0335 (<0.36, good stability), 0.1240 (0.36, poor stability), respectively. The research results of this paper provide a new strategy for detecting weld stability.