We propose a multiple-image encryption (MIE) scheme based on triple interferences for flexibly decrypting high-quality images. Each image is discretionarily deciphered without decrypting a series of other images earlier. Since it does not involve any cascaded encryption orders, the image can be decrypted flexibly by using the novel method. Computer simulation demonstrated that the proposed method’s running time is less than approximately 1/4 that of the previous similar MIE method. Moreover, the decrypted image is perfectly correlated with the original image, and due to many phase functions serving as decryption keys, this method is more secure and robust.

We combine the 3D hybrid-Weickert nonlinear diffusion regularizer with digital holography, which can distinguish the locations of certain small-sized particles that are overlapped along the z -axis, to realize autofocusing of multiple micro-objects and simultaneously remove the defocused images. The proposed method is applied to all the reconstruction slices generated from the hologram after each back-propagation. After applying the proposed scheme for certain small-sized particles, the reconstructed image at the ground truth z position is retained, the defocused images are diffused out when the reconstruction range along the z -axis is sufficiently long and the reconstruction depth spacing is sufficiently fine. The results demonstrated that the proposed scheme can diffuse out the defocused images that are 20 μ m from the ground truth z position, despite several scattering particles of different diameters being completely overlapped along the z -axis with a distance of 800 μ m when the hologram pixel pitch is 2 μ m. In addition, the results revealed that the sparsity distribution of the ground truth z slice is not affected by the sparsity distribution of the corresponding defocused images when the diameter of the particle is less than 35 μ m and the reconstruction depth spacing is more than 20 μ m.

Digital holographic imaging can capture a volume of a particle field and reconstruct three-dimensional (3D) information of the volume from a two-dimensional (2D) hologram. However, it experiences a DC term, twin-images, defocus images of other particles and noise induced by the optical system. We propose the use of a U-net model to extract in-focus particles and encode the in-focus particles as squares at ground truth z. Meanwhile, zero-order images, twin-images, defocused images of other particle and noise induced by the optical system are filtered out. The central coordinate of the square represents the lateral position of the particle, and the side length of the square represents the particle diameter. The 2D raw-reconstructed images generated from the pre-processed hologram by utilizing backward Fresnel propagation serve as the input of the network. A dense block is designed and added to the encoder and decoder of the traditional U-net model. Each layer takes the inputs from all previous layers and passes the feature maps to all subsequent layers, thereby facilitating full characterization of the particles. The results show that the proposed U-net model can extract overlapping particles along the z-axis well, allowing the detection of dense particles. The use of that squares characterize particles makes it more convenient to obtain particle parameters.

Although the existing digital holographic technologies are effective in measuring particle concentration, the processes are cumbersome and time-consuming. The purpose of this study is to quickly and accurately measure the particle number from a single hologram through deep learning. The simulation and experimental results show that the prediction number are close to the ground truth without the complicated reconstructions and denoising processes, and the average relative error remains less than 10%. The prediction time of a hologram is at the millisecond level, which offers a new possibility for real-time processing.

Impervious surface(s) (ISs) as an important ecological index has been widely used in the study of urban ecological environment evaluation, urban hydrology and urban vegetation mapping. In this paper, methods in feature selection, including principal component analysis (PCA) and optimum index factor (OIF), as well as indices approaches such as the normalized difference impervious surface index (NDISI) and normalized difference built-up index (NDBI) were evaluated. A new approach, experimental band combination (EBC), was proposed to extract ISs in Taiyuan city of Shanxi Province, China, using Landsat Thematic Mapper (TM) imagery. The same classification procedure was applied on original TM image, PCA image, OIF image and EBC image respectively to extract IS; the results were also compared with those extracted from the aforementioned indices approaches. Accuracy assessments were conducted on each of them using 253 randomly selected sampling points. By comparison and analysis, it was found that the EBC method obtained the highest producer’s accuracy (87.63%), user’s accuracy (90.43%) and overall accuracy (87.75%), with the Kappa statistic of 0.85 in IS extraction.

We propose a modified residual neural network (ResNet) to quickly and accurately detect the particle quantity from raw reconstructed images of a high-density particle solution in digital holography. The raw reconstructed image is fed into the modified ResNet to obtain the particle quantity. Then, the quantity and particle concentration in the captured volume are calculated. The metrological challenge is modeled as a regression problem in deep learning. The average relative error of the holograms in the test dataset is less than 10% even when predicting the particle quantities untrained by the model. Hence, an accurate particle quantity is obtained even when the raw reconstructed images are not denoised, thereby reducing the processing time.