With the application of target detection technology in UAVs and other aircraft, the demand for the analysis of aerial images is increasing year by year. Focusing on the single-stage target detection algorithm in deep learning, the current mainstream deep learning single-stage target detection algorithm is first introduced; the basic principle and optimization process of the single-stage series target detection algorithm are sorted out, and a systematic summary of the research progress of aerial image detection by Single-Stage series algorithm is analyzed. Finally, the future direction of the Single-stage series algorithm in aerial image detection prospects.
The atmosphere contains many tiny, suspended particles, and due to the scattering and absorption of these particles, images can show reduced visibility, distorted colors’, blurred details and other situations. Many computer vision applications are unable to accept these degraded images and therefore require high quality input images to ensure accurate work, provided by a defogging method. Single image deblurring utilizes physical models where transmission estimation is an important parameter in obtaining a fog-free image. The fog image is analyzed and pre-processed to highlight details and make it more suitable for human and machine recognition. The analysis of different deblurring methods divides them mainly into methods based on image a priori recovery, image enhancement and deep learning. The content of defogging-related algorithms is described, and future directions are analyzed.
With the continuous development of information technology, digital signal processing has been paid more and more attention. Aiming at the visualization of spectrum analysis and power spectrum estimation in digital signal processing, this paper designs and develops a simulation platform with the help of MATLAB GUI. The simulation platform has simple operation, intuitive display, comprehensive functions, good interactivity and strong intuition, and can visualize abstract and difficult algorithms.
Since human visual properties have ambiguity, the neighborhood information of pixels in an image also has ambiguity. Using fuzzy theory for image enhancement allows mathematics to learn the advantages of the human brain in recognition and judgment of ambiguous phenomena, and use precise mathematical methods to deal with things that cannot be accurately represented by mathematics. In the article, the basic concepts of fuzzy theory, several common fuzzy distributions are introduced, and image transform domain enhancement algorithm based on fuzzy theory are studied. It is shown that the fuzzy theory algorithm can improve the contrast of useful information of images and facilitate people to get useful information more quickly.
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