Spectral confocal technology uses chromatic aberration which is generated by a dispersion lens to detect surface shape. The axial dispersion generated by the dispersion lens will affect the measurement range of the whole spectral confocal displacement sensor. The refractive index of an axial GRIN (gradient index, GRIN) lens varies non-homogeneous along the axial direction and is constantly perpendicular to an optical axis of the plane. The paper explored the design of a dispersive objective lens for a spectral confocal displacement sensor based on the GRIN lens. Firstly, the optical power and axial dispersion models of the GRIN lens are established. The axial dispersion can be realized by focusing the light of different wavelengths at different positions of the optical axis. Secondly, based on the optic power and dispersion function of the GRIN lens, the refractive index distribution of the GRIN lens and the simulation design of the dispersive objective lens is obtained by using MATLAB and ZEMAX software respectively. Finally, the GRIN dispersive objective lens is optimized by setting different merit function operands. The experimental results show that the axial GRIN lens can achieve a focal shift of 1130μm within the wavelength range from 486nm to 656nm. Moreover, the linearity of the lens behaves well. All the blur spot is much smaller than the airy spot. The lens has well-focused as well as high-precision. The research results provide a reference and theoretical basis for the application of the GRIN lens in spectral confocal technology.
The chromatic confocal technology (CCT) uses the dispersion principle to establish an accurate encoding relationship between the spatial position and the axial focus point of each wavelength to achieve non-contact measurement. The accuracy of the measurement results is affected by the peak wavelength extraction accuracy. The flexible and adaptable characteristics of machine learning techniques are used to model the spectral wavelength and light intensity nonlinearly, establish the response relationship between input wavelength and output normalized light intensity, and refit the spectral curve distribution. In this paper, we apply the network of regression aspect of machine learning, Extreme Learning Machine (ELM), Back Propagation Neural Network (BPNN), and Genetic Algorithm optimized Back Propagation Neural Network(GA-BPNN) to fit the spectral response of the system to accurately locate the peak wavelength and compare it with the traditional peak extraction methods of Gaussian fitting, polynomial fitting, and center of the mass method to verify that the machine learning method used is significantly better than the traditional peak extraction methods in terms of peak extraction accuracy. The ELM network is the best among the three networks, with a peak extraction error of only 0.04μm and a Root Mean Square Error(RMSE) of only 6.8×10-4. The analysis of calibration experiments, resolution, and stability experiments found that the ELM algorithm was found to have the shortest calculation time, and the system measurement resolution was explored through the ELM algorithm to be about 2μm. The research results of this paper have contributed to the improvement of the system measurement accuracy and measurement efficiency.
The chromatic confocal technology (CCT) has ultra-high distance measurement resolution and the characteristics of multi-surface tomography. It is merely widely used to measure the thickness of uniform materials currently. As a typical inhomogeneous material, the iso-refractive index surface of the radial GRIN lens is a cylindrical surface with central axis symmetry. The radial GRIN lens is an important optical element in the field of micro integrated optical instruments, such as optical fiber sensing, optical communication, etc. High precision thickness measurement parameters help to guide the accurate application of the GRIN lens and control the performance of related ultra-precision optical instruments. To measure the thickness of the radial GRIN lens with a single probe by the advantage of the CCT technology in measuring tiny distances. In addition, the placement tilt of the GRIN lens during the measurement would change the incident position of the probe light entering the lens to change the propagation path of the light and inevitably affect the accuracy of thickness measurement. The influence of the GRIN lens placement tilt on thickness measurement is studied theoretically. The thickness measurement error caused by the inclination of the GRIN lens and its axial measurement position is simulated and analyzed. The research results have significance for optimizing the system structure and further improving the system performance for the application of the CCT in measuring non-homogeneous materials or optical thin film.
This paper aims at the application requirements of target localization in specific environments, a method based on single scattering of polarized ultraviolet light to achieve non-line-of-sight target localization is proposed. In this paper, the target positioning is completed by combining with the obtained target azimuth angle and distance. Firstly, in this paper, based on the non-coplanar single scattering channel model of a spherical coordinate system, combining with the transmission characteristic of atmospheric scattering of polarized ultraviolet light, the single scattering model of polarized ultraviolet light is established for the non-line-of-sight targets which are not at the same height. The polarization scattering transmission characteristics of ultraviolet light and the variation of the radiation intensity were analyzed by the method of matrix optics. Thus a relationship between the received light intensity and the azimuth angle and the distance between transmitter and receiver was established. Then, matlab software is used to simulate and analyze the effect of the distance between transmitter and receiver on the received light intensity under the condition of the maximum receiving light intensity during sunny and smog days. Finally, the effect of the elevation error of the receiver and the transmitter on target positioning is simulated and analyzed. At the distance between transmitter and receiver of 400m, the distance measurement error is 20.6m on sunny days and 26m on smog days when the transmitter and receiver elevation angle deviates 3° (the preset transmit elevation angle is 35° and receive elevation angle is 25°). In addition, the background light affects determination of the azimuth angle but has a small impact on the ranging. In order to achieve accurate target positioning, this paper proposes effective improvement measures for the above possible errors.
Remote sensing image combines the characteristics of visible light image, infrared image and other multi-spectral images, making it rich in details and low resolution. However, due to factors such as weather and transmission errors, salt and pepper noise is prone to occur, and it is difficult to effectively detect weak edges. Aiming at the problems, a morphological edge detection algorithm based on hierarchical multi-scale is proposed in this paper. Firstly, the adaptive median filter is used to smooth the remote sensing image. Secondly, it is proposed to use the mutual information of images as the cost function for processing, and the multi-scale hierarchical ratio is 2 1 . Then, the four directional structural elements of 0 , 45 , 90 and 135 are used to extract the original-scale and small-scale image edges respectively. Calculate the sum of the gray difference values of eight neighborhoods of the edge points, thus calculate the direction adaptive weight, and then the edge detection results are obtained by fusion respectively. Finally, the pixels in the edge image are classified, and the fusion method of enhancing edge and weak edge and filtering false edge is proposed, and then the edge detection image is obtained. Aiming at the application of remote sensing images, the comparison of the results shows that the proposed algorithm has stronger anti-noise performance, the weak edge detection ability is improved, thus avoiding missed detection and false detection of edge information, and detecting more complete and accurate edge details.
The spectral absorption characteristics of hemoglobin determine that the contrast between R and B components in the white light endoscopic blood vessel image is poor, and the blood vessel features in the G component are the clearest and the contrast is good. Based on this feature, this paper proposes to use the G-component image to perform nonlinear stretching to obtain the stretched image, and subtract the original image from the stretched image to obtain the G-component high-frequency detail image containing blood vessel feature information; Then, using the high-frequency detail image to perform unsharp mask processing on the R, G, and B components of the original image, respectively, to obtain a blood vessel contrast-enhanced image; In order not to cause grayscale dispersion in the transition zone of the blood vessel edge during stretching, Performance simulation experiments are carried out for endoscopic images of fundus and oral cavity. The results show that the proposed algorithm not only improves the image contrast, but also has a better enhancement effect on small blood vessels with inconspicuous original features. By comparing with the performance of Spectra B and the method of literature [6], the average gradient value of the algorithm in this paper is increased by about 300%, the information entropy value is increased by about 30%, and the DV-BV value is increased by about 75%.
The chromatic confocal technology (CCT) has ultra-high distance measurement resolution and the characteristics of multi-surface tomography. Based on the principle of optical dispersion and confocal, the technology achieves accurate axial position or micro-displacement measurement. The radial gradient index (GRIN) lens is a typical important inhomogeneous material. Its refractive index gradually decreases from the center to the edge along the radial direction. We propose to measure the thickness of the radial GRIN lens based on CCT. The thickness measurement model is established by the ray-tracing method, the optical Lagrange function, and the ray arc differential in the Cartesian coordinate system. The refractive index distribution makes the eccentric tilt of the lens change the propagation of the probe light and affects the extraction of the peak of the spectral response causing the thickness measurement error. The influence of eccentricity and tilt of the radial GRIN lens on its thickness measurement is studied. The precision shift table drives the radial GRIN lens movement to simulate the eccentric tilt state of the lens under the dispersion probe. According to the comparative analysis between the experimental and simulation results, it is concluded that the greater the eccentric distance and inclination degree of the radial GRIN lens is, the greater the influence on the thickness measurement is. The larger the lens thickness is, the greater the measurement error is. For the radial GRIN lens with a thickness of 2.36 mm, the measurement error is about 20 μm when the eccentricity is 0.1 mm and the tilt is 6 deg. The research results have significance for optimizing the system structure and further improving the system performance for the application of the CCT in measuring non-homogeneous materials or optical thin film. And the research will encourage the development of GRIN lens instrument preparation and application technology by improving the thickness measurement accuracy and precision of the GRIN lens.
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