ABSTRACT
The conventional reflective optical surface with adjustable reflection characteristics requires a complex external power source. The complicated structure and preparation process of the power system leads to the limited modulation of the reflective properties and difficulty of use in large-scale applications. Inspired by the biological compound eye, different microstructures are utilized to modulate the optical performance. Convex aspheric micromirror arrays (MMAs) can increase the luminance gain while expanding the field of view, with a luminance gain wide angle > 90° and a field-of-view wide angle close to 180°, which has the reflective characteristics of a large gain wide angle and a large field-of-view wide angle. Concave aspheric micromirror arrays can increase the luminance gain by a relatively large amount of up to 2.66, which has the reflective characteristics of high gain. Industrial-level production and practical applications in the projection display segment were carried out. The results confirmed that convex MMAs are able to realize luminance gain over a wide spectrum and a wide range of angles, and concave MMAs are able to substantially enhance luminance gain, which may provide new opportunities in developing advanced reflective optical surfaces.
ABSTRACT
With the development of remote sensing technology, true-color visualization of hyperspectral LiDAR echo signals has become a hotspot for both academic research and commercial applications. The limitation of the emission power of hyperspectral LiDAR causes the loss of spectral-reflectance information in some channels of the hyperspectral LiDAR echo signal. The color reconstructed based on the hyperspectral LiDAR echo signal is bound to have serious color cast problem. To solve the existing problem, a spectral missing color correction approach based on adaptive parameter fitting model is proposed in this study. Given the known missing spectral-reflectance band intervals, the colors in incomplete spectral integration are corrected to accurately restore target colors. Based on the experimental results, the color difference between color blocks and the hyperspectral image corrected by the proposed color correction model is smaller than that of the ground truth, and the image quality is higher, realizing the accurate reproduction of the target color.
ABSTRACT
Aiming at the situation that the existing visible and infrared images fusion algorithms only focus on highlighting infrared targets and neglect the performance of image details, and cannot take into account the characteristics of infrared and visible images, this paper proposes an image enhancement fusion algorithm combining Karhunen-Loeve transform and Laplacian pyramid fusion. The detail layer of the source image is obtained by anisotropic diffusion to get more abundant texture information. The infrared images adopt adaptive histogram partition and brightness correction enhancement algorithm to highlight thermal radiation targets. A novel power function enhancement algorithm that simulates illumination is proposed for visible images to improve the contrast of visible images and facilitate human observation. In order to improve the fusion quality of images, the source image and the enhanced images are transformed by Karhunen-Loeve to form new visible and infrared images. Laplacian pyramid fusion is performed on the new visible and infrared images, and superimposed with the detail layer images to obtain the fusion result. Experimental results show that the method in this paper is superior to several representative image fusion algorithms in subjective visual effects on public data sets. In terms of objective evaluation, the fusion result performed well on the 8 evaluation indicators, and its own quality was high.
