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To improve the fusion performance of infrared and visible images and effectively retain the edge structure information of the image, a fusion algorithm based on iterative control of anisotropic diffusion and regional gradient structure is proposed. First, the iterative control operator is introduced into the anisotropic diffusion model to effective...
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Infrared image has lower resolution, lower contrast, and less detail than visible image, which causes its super-resolution (SR) more difficult than visible image. This paper presents an approach based on a deep neural network that comprises an image SR branch and a gradient SR branch to reconstruct high-quality SR image from single-frame infrared i...
Citations
... In recent years, Unmanned Aerial Vehicles (UAV) have played an increasingly important role in many fields due to their high flexibility, low cost, and easy operation [1]. In the military, they are often utilized to perform reconnaissance, battlefield situation monitoring, and other tasks. ...
Image registration is the base of subsequent image processing and has been widely utilized in computer vision. Aiming at the differences in the resolution, spectrum, and viewpoint of infrared and visible images, and in order to accurately register infrared and visible images, an automatic robust infrared and visible image registration algorithm, based on a deep convolutional network, was proposed. In order to precisely search and locate the feature points, a deep convolutional network is introduced, which solves the problem that a large number of feature points can still be extracted when the pixels of the infrared image are not clear. Then, in order to achieve accurate feature point matching, a rough-to-fine matching algorithm is designed. The rough matching is obtained by location orientation scale transform Euclidean distance, and then, the fine matching is performed based on the update global optimization, and finally, the image registration is realized. Experimental results show that the proposed algorithm has better robustness and accuracy than several advanced registration algorithms.
