计算机科学 ›› 2024, Vol. 51 ›› Issue (12): 199-208.doi: 10.11896/jsjkx.231000187
司伟纳, 叶军, 姜斌
SI Weina, YE Jun, JIANG Bin
摘要: 高光谱图像去噪是遥感领域的一个基本问题,也是预处理的重要步骤。基于代表系数全变分的去噪方法在高光谱图像(HSI)去噪中有着广泛的应用。代表系数矩阵U继承了干净HSI的先验信息,能够实现全局低秩并降低计算复杂度,但由于一阶全变分的引入,该类方法在去噪过程中产生了很强的阶梯效应并且忽略了不同波段间的共同特征,因此去噪效果很差。针对此问题,提出了一种新的联合群稀疏和代表系数双向空间光谱全变分(RCBGS)的正则化去噪模型。高阶全变分的引入缓解了阶梯效应,并在子空间的差分上引入加权$\ell$2,1范数,充分挖掘不同波段除全局低秩外的共同特征,提高了HSI的内在群稀疏性和整体光滑性。最后,通过交替方向乘子法(ADMM)给出了所提方法的迭代规则,且所提方法的评价指标峰值信噪比相对于对比方法平均提升了8.79%。在模拟和真实数据集上的实验表明,所提方法在视觉质量和定量评估方面都优于相关方法。
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