For traditional HDR fusion algorithm based on mapping functions, the artificial determination of the over exposure region often ignores the spatial distribution and the relation of pixels of the image brightness. In this paper, a stimulated HDR fusion algorithm based on pulse coupled neural network is proposed. For the proposed algorithm, first obtain the external stimuli according to the maximum image brightness, calculate whether the pixel “fires” or not and determine the over exposure region. Then alter the value of external stimuli to perform iteration and construct judgment matrix to make sure that every pixel in the image “fires”. The image irradiance is linear with the illuminance and exposure time. Obtain the brightness mapping function using linear fitting, and synthetize HDR image with over exposure correction algorithm to eliminate artifacts. The algorithm mainly has the following advantages: 1. It fully considers the spatial distribution of the image brightness. 2. For the imaging of different scenes, it self-adapts to separate exposure regions. 3. It performs mapping according to the imaging principles, and obtains the mapping function without the exposure time. 4. It reduces the quantitative error of synthetizing image, and the artifacts in the transition region. The algorithm proposed in this paper is experimented under multiple scenes, and the image entropy and spatial frequency are improved greatly.
A certain model should be used to predict and track the space objects that may affect the operation of satellites. In this paper, the Two-Line Elements (TLE) and the Simplified General Perturbations 4 (SGP4) models are used to calculate the position and velocity vectors of satellite A/B in the True Equator Mean Equinox (TEME) coordinate system and the Earth-Centered Inertial (ECI) system. Satellite A is selected as the observation platform to carry the camera, and the imaging positions of target satellite B on the camera of satellite A in the TEME and ECI coordinate systems are calculated respectively. The imaging trajectories are superimposed to generate several turns. The difference between the imaging positions in TEME and ECI coordinate systems is calculated, and the influence of the Earth's precession and nutation on the imaging trajectory is analyzed.
With the development of the next generation of intelligent battlefield situation awareness technology, infrared multi-target tracking plays an increasingly important role in complex background. However, the commonly used infrared target tracking algorithm, weak small target enhanced motion information ignores the apparent feature, large target enhanced apparent feature ignores the motion information. To solve the above problems, this paper proposes a target tracking method based on the fusion of location, detection and feature matching, constructs the target motion information predictor information and target detection response, so as to achieve fast target tracking. Firstly, the Bayesian multi-target filter is used, and the weight factor of the corresponding Gaussian component is added to the Kalman filter to obtain the number and state set of the targets in the scene at a certain time, and the target position predictor is established to complete the primary correlation based on the fast position prediction. Then, according to the feature distribution of the detection response, the secondary correlation based on the effective features of the targets is completed, Form the final complete track of the target. In this paper, the multi-target complex scene multi-target motion environment simulation experiments, the experimental results show that the algorithm can better track the target in complex motion environment.
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