In order to improve the accuracy of polarization target detection, the multi-parameter polarization contrast model is proposed after analyzing the typical polarization features of the polarization images. It utilizes both of the polarization degree and the polarization angle parameters. Then the fast polarizer angle detection method is designed according to this model to calculate and drive the motor to rotate the polarizer to the most appropriate deviation angle so as to maximize the contrast between the target and the background. Experimental results show that the proposed method can improve the contrast between the target and the background in the polarized image significantly, which makes the polarization detection more efficiently and lays a foundation for detecting the moving targets.
Because the images are always contaminated by different kinds of noise in the courses of image acquisition, transmission and storage process, the image denoising is a very important step of image restoration. The key of denoising algorithm is making recovery image reserve as much as possible edge details when eliminating noise. Because noise and image details both are part of the high frequency components of image, to some extent, these two sides are contradictory. If the selection of the criterion and treatment for noise and marginal are inappropriate , denoising will make image details ( especially the marginal) become more vague, which must reduce the quality of the image and increase greatly the complexity of subsequent image processing. Since the quantum process and imaging process have the similar characteristics in the probability and statistics fields, a kind of soft threshold denoising algorithm is proposed based on the concept of quantum computation such as the quantum bit, superposition and collapse, etc. This filter algorithm can generate an adaptive template according to the characteristic of the edge of local image. Due to the algorithm is sensitive to the shape of edge, the balance is obtained between the noise suppression and the edge preserving.
On-orbit Modulation Transfer Function (MTF) is an important indicator to evaluate the performance of the optical
remote sensors in a satellite. There are many methods to estimate MTF, such as pinhole method, slit method and so on.
Among them, knife-edge method is quite efficient, easy-to-use and recommended in ISO12233 standard for the wholefrequency
MTF curve acquisition. However, the accuracy of the algorithm is affected by Edge Spread Function (ESF)
fitting accuracy significantly, which limits the range of application. So in this paper, an optimized knife-edge method
using Powell algorithm is proposed to improve the ESF fitting precision. Fermi function model is the most popular ESF
fitting model, yet it is vulnerable to the initial values of the parameters. Considering the characteristics of simple and
fast convergence, Powell algorithm is applied to fit the accurate parameters adaptively with the insensitivity to the initial
parameters. Numerical simulation results reveal the accuracy and robustness of the optimized algorithm under different
SNR, edge direction and leaning angles conditions. Experimental results using images of the camera in ZY-3 satellite
show that this method is more accurate than the standard knife-edge method of ISO12233 in MTF estimation.
During traditional multi-resolution infrared and visible image fusion processing, the low contrast ratio target may be weakened and become inconspicuous because of the opposite DN values in the source images. So a novel target pseudo-color enhanced image fusion algorithm based on the modified attention model and fast discrete curvelet transformation is proposed. The interesting target regions are extracted from source images by introducing the motion features gained from the modified attention model, and source images are performed the gray fusion via the rules based on physical characteristics of sensors in curvelet domain. The final fusion image is obtained by mapping extracted targets into the gray result with the proper pseudo-color instead. The experiments show that the algorithm can highlight dim targets effectively and improve SNR of fusion image.
Aiming at the nonlinear and non-Gaussian features of the real infrared scenes, an optimal nonlinear filtering based algorithm for the infrared dim target tracking-before-detecting application is proposed. It uses the nonlinear theory to construct the state and observation models and uses the spectral separation scheme based Wiener chaos expansion method to resolve the stochastic differential equation of the constructed models. In order to improve computation efficiency, the most time-consuming operations independent of observation data are processed on the fore observation stage. The other observation data related rapid computations are implemented subsequently. Simulation results show that the algorithm possesses excellent detection performance and is more suitable for real-time processing.
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