利用地基观测相机拍摄的以深空为背景的星图受星空复杂背景的影响,往往具有较高的噪声水平.同时由于星图主要由恒星、空间目标和星空背景噪声组成,且成点状分布,星图目标和噪声呈现较大的相似性,传统的图像去噪算法并不适用于星图.为此,该文提出一种基于能量函数的极值中值滤波去噪算法,该算法在去除星图椒盐噪声的同时能够较好地保持图像目标信息.该方法针对疑似噪声点采用二次检测的方式,并且结合改进的自适应中值滤波和能量函数模型进行灰度值恢复.该文分别使用仿真试验和真实星图处理试验对该方法进行验证,在客观评价中,图像峰值信噪比PSNR(Peak Signal to Noise Ratio)最高可提高3倍多,均方误差MSE(Mean Squared Error)减小为加噪图像的53.16×10-.试验结果表明,该方法可有效地降低传统方法的噪声误检问题,同时提高噪声图像的恢复精度,非常适合星图噪声的去除.%The star maps acquired by the ground-based cameras are susceptible to the complex background of the starry sky and thus have high noise levels. In addition, the targets in star maps are similar to the noises due to their punctate shapes. As a result, the traditional image denoising method is not applicable to star maps. A new adaptive extremum median filtering denoising algorithm is put forward based on energy function, which can effectively remove the salt and pepper noise of the star maps and keep the small target information at the same time. This method employs a twice-check strategy to reduce the false detection ratio of noisy pixels and uses the improved adaptive median filter and the energy function model to recovery noise imagery. The simulated and real star map experiments show that, the Peak Signal to Noise Ratio (PSNR) is improved about 3 times and the Mean Squared Error (MSE) is reduced by53.16×10- in the terms of objective evaluations, the proposed method can effectively improve the denoising result and thus is applicable to star maps.
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