Hyperspectral Acquisition Technology Based on Compressed Sampling in Spatial Domain

In the hyperspectral imaging device, the sensor detects the reflection or radiation intensity of the target at hundreds of different wavelengths, thus forming a spectral image composed of hundreds of continuous bands. The traditional processing method of sampling first and then compressing not only cannot fundamentally solve the problem of huge amount of data, but also causes waste of resources. To solve this problem, a spectral image reconstruction method based on compressed sampling in spatial domain and transform coding in spectral domain is designed by using the sparsity of single-band two-dimensional image and the spectral redundancy of spatial coded data. Based on Bayesian theory, a compressed sensing measurement matrix of adaptive projection is proposed. Combining these two algorithms, an adaptive Grouplet-FBCS algorithm is constructed to reconstruct the image using smooth projection Landweber. Experimental results show that, compared with existing image block compression sensing algorithms, this algorithm can significantly improve the quality of image signal reconstruction.

The method of repairing OHE damaged ancient painted murals based on machine vision

Traditional mural repair methods only observe the texture of murals when segmenting the repair area, but ignore the extraction of a mural damage data, resulting in incomplete damage crack information. For this reason, the method of repairing the damaged murals based on machine vision is studied. Using machine vision, it can get two-dimensional image of a mural, preprocess the image, extract the damaged data of a mural, and then divide the repair area and repair degree index. According to different types of damage, it can choose the corresponding repair methods to achieve the repair of damaged mural. The results show: Compared with the reference [1] method and reference [2] method, the number of repair points and repair cracks extracted by the proposed method is more than that of the two traditional methods, which can more accurately and comprehensively extract the repair information of murals.

Light-Addressable Potentiometric Sensors in Microfluidics

Light-addressable potentiometric sensor (LAPS) is an electrochemical sensor based on the field-effect principle of semiconductor. It is able to sense the change of Nernst potential on the sensor surface, and the measuring area can be controlled by the illumination. Due to the unique light-addressable ability of LAPS, the chemical imaging system constructed with LAPS can realize the two-dimensional image distribution detection of chemical/biomass. In this paper, the advantages of LAPS as sensing unit of microelectrochemical analysis system are summarized. Then, the greatest development of LAPS analysis system is explained and discussed. Especially, this paper focused on the research of ion diffusion, enzymatic reaction, microbial metabolism and droplet microfluidics by using LAPS analysis system. Finally, the development trends and prospects of LAPS analysis system are illustrated.

Multi-Frequency Image Completion via a Biologically-Inspired Sub-Riemannian Model with Frequency and Phase

We present a novel cortically-inspired image completion algorithm. It uses five-dimensional sub-Riemannian cortical geometry, modeling the orientation, spatial frequency and phase-selective behavior of the cells in the visual cortex. The algorithm extracts the orientation, frequency and phase information existing in a given two-dimensional corrupted input image via a Gabor transform and represents those values in terms of cortical cell output responses in the model geometry. Then, it performs completion via a diffusion concentrated in a neighborhood along the neural connections within the model geometry. The diffusion models the activity propagation integrating orientation, frequency and phase features along the neural connections. Finally, the algorithm transforms the diffused and completed output responses back to the two-dimensional image plane.

Semi-Automated Roller Parameters Extraction from Terrestrial Lidar

Rollers are an integral part of a hot-rolling steel mill. They transport hot metal from one end of the mill to another. The quality of the steel highly depends on the surface quality of the rollers. This paper presents semi-automated methodologies to extract roller parameters from terrestrial lidar points. The procedure was divided into two steps. First, the three-dimensional points were converted to a two-dimensional image to detect the extents of the rollers using fast Fourier transform image matching. Lidar points for every roller were iteratively fitted to a circle. The radius and center of the fitted circle were considered as the average radius and average rotation axis of the roller, respectively. These parameters were also extracted manually and were compared to the measured parameters for accuracy analysis. The proposed methodology was able to extract roller parameters at millimeter level. Erroneously identified rollers were identified by moving average filters. In the second step, roller parameters were determined using the filtered roller points. Two data sets were used to validate the proposed methodologies. In the first data set, 366 out of 372 rollers (97.3%) were identified and modeled. The second, smaller data set consisted of 18 rollers which were identified and modelled accurately.

Influence of pressure on the wake of a hydrophobic circular cylinder for a flow regime with Reynolds number of 2.2×105

Vortex flow structures in a turbulent wake behind a circular Teflon cylinder immersed in an incoming flow with a change in pressure for the Reynolds number Re = 2.2×105 have been experimentally studied using a two-dimensional image (2D-PIV) of particles in a closed-circuit water tunnel. The obtained results are presented in the form of time-averaged velocity fields, Reynolds stresses, and distributions of turbulent kinetic energy. The flow data showed that the size of the wake flow region, Reynolds stresses and turbulent kinetic energy change depending on the pressure in the flow. As a result of a 20% reduction in pressure, the size of the vortex zone in the wake increases by about 20%.

High-NA optical edge detection via optimized multilayer films

There has been a significant effort to design nanophotonic structures that process images at the speed of light. A prototypical example is in edge detection, where photonic-crystal-, metasurface-, and plasmon-based designs have been proposed and in some cases experimentally demonstrated. In this work, we show that multilayer optical interference coatings can achieve visible-frequency edge detection in transmission with high numerical aperture, two-dimensional image formation, and straightforward fabrication techniques, unique among all nanophotonic approaches. We show that the conventional Laplacian-based transmission spectrum may not be ideal once the scattering physics of real designs is considered, and show that better performance can be attained with alternative spatial filter functions. Our designs, comprising alternating layers of Si and SiO2 with total thicknesses of only 1 µm, demonstrate the possibility for optimized multilayer films to achieve state-of-the-art edge detection, and, more broadly, analog optical implementations of linear operators.

Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning

Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. While current cardiovascular treatments can assist to reduce the risk of this disease, a large number of patients still retain a high risk of experiencing a life-threatening cardiovascular event. Thus, the advent of new treatments methods capable of reducing this residual risk remains an important healthcare objective. This paper proposes a deep learning-based method for section recognition of cardiac ultrasound images of critically ill cardiac patients. A convolution neural network (CNN) is used to classify the standard ultrasound video data. The ultrasound video data is parsed into a static image, and InceptionV3 and ResNet50 networks are used to classify eight ultrasound static sections, and the ResNet50 with better classification accuracy is selected as the standard network for classification. The correlation between the ultrasound video data frames is used to construct the ResNet50 + LSTM model. Next, the time-series features of the two-dimensional image sequence are extracted and the classification of the ultrasound section video data is realized. Experimental results show that the proposed cardiac ultrasound image recognition model has good performance and can meet the requirements of clinical section classification accuracy.