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Super-resolution method based on sparse reconstruction is an effective way to deal with the closely spaced objects problem, but when the targets in a noisy environment, the noise will cover over the entire field, leading to the sparsity feature of the original scene is destroyed. Aiming at this phenomenon, this paper proposed a super-resolution method which has the adaptative reconstruction ability in noisy environments, this method takes full advantage of the structural characteristics of the sensor and the reconstruction algorithm parameters, through the establishment of infrared imaging model of the observed signals and pixel meshing, establishment of the position and amplitude of the closely spaced objects of sparse representation, and using the point spread function of the optical system to construct over-complete dictionary, the last step is making the reconstruction parameters in a reasonable range through controlling the ratio of non-zero elements in the rebuilt scene, so as to achieve the purpose of removing noise interference and reconstruction of sparse targets accurately. Simulation results show that the proposed method with adaptive reconfiguration in noisy environments.
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