Photographic images taken in foggy or hazy weather (hazy images) exhibit poor visibility and detail because of scattering and attenuation of light caused by suspended particles, and therefore, image dehazing has attracted considerable research attention. The current polarization-based dehazing algorithms strongly rely on the presence of a "sky area", and thus, the selection of model parameters is susceptible to external interference of high-brightness objects and strong light sources. In addition, the noise of the restored image is large. In order to solve these problems, we propose a polarization-based dehazing algorithm that does not rely on the sky area ("non-sky"). First, a linear polarizer is used to collect three polarized images. The maximum- and minimum-intensity images are then obtained by calculation, assuming the polarization of light emanating from objects is negligible in most scenarios involving non-specular objects. Subsequently, the polarization difference of the two images is used to determine a sky area and calculate the infinite atmospheric light value. Next, using the global features of the image, and based on the assumption that the airlight and object radiance are irrelevant, the degree of polarization of the airlight (DPA) is calculated by solving for the optimal solution of the correlation coefficient equation between airlight and object radiance; the optimal solution is obtained by setting the right-hand side of the equation to zero. Then, the hazy image is subjected to dehazing. Subsequently, a filtering denoising algorithm, which combines the polarization difference information and block-matching and 3D (BM3D) filtering, is designed to filter the image smoothly. Our experimental results show that the proposed polarization-based dehazing algorithm does not depend on whether the image includes a sky area and does not require complex models. Moreover, the dehazing image except specular object scenarios is superior to those obtained by Tarel, Fattal, Ren, and Berman based on the criteria of no-reference quality assessment (NRQA), blind/referenceless image spatial quality evaluator (BRISQUE), blind anistropic quality index (AQI), and e.
A graphene oxide film was formed on the PEO coatings of magnesium alloys via an electrostatic self-assembly method, which functioned as a physical separation with inhibiting effects between the protected metal and reactants.
The axial imaging range of optical microscopy is restricted by its fixed working plane and limited depth of field. In this paper, the axial capabilities of an off-the-shelf microscope is improved by inserting a liquid lens, which can be controlled by a driving electrical voltage, into the optical path of the microscope. First, the numerical formulas of the working distance and the magnification with the variation of the focus of the liquid lens are inferred using a ray tracing method and conclusion is obtained that the best position for inserting a liquid lens with consistent magnification is the aperture plane and the rear focal plane of the objective lens. Second, with the liquid lens embedded in the microscope, the numerical relationship between the magnification and the working distance of the proposed flexible-axial-capability microscope and the liquid lens driving voltage is calibrated and fitted using the inferred numerical formulas. Third, techniques including autofocus, extending depth of field and three-dimensional imaging are researched and applied, improving the designed microscope to not only flexibly control its working distance, but also to extend the depth of field near the variable working plane. Experiments show that the presented flexible-axial-capability microscope has a long working distance range of 8 mm, and by calibrating the magnification curve within the working distance range, samples can be observed and measured precisely. The depth of field can be extended to 400 μm from the variable working plane and is 20 times that of the off-the-shelf microscope.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.