计算机科学 ›› 2023, Vol. 50 ›› Issue (6): 209-215.doi: 10.11896/jsjkx.220300236
张历洪, 叶军
ZHANG Lihong, YE Jun
摘要： 高光谱图像(Hyperspectral Image,HSI)在采集过程中会产生多种类型的噪声,噪声数量越多,HSI的有效信息就越少。为了更有效地从大量混合噪声中恢复HSI的有效消息,文中提出了一种基于群稀疏正则化的约束平滑秩近似HSI恢复方法。其中,群稀疏正则化被定义为基于加权$\ell_{2,1}$范数的空谱全变分,该正则化在利用空谱维信息的同时也考虑到了HSI内部的群稀疏性,增强了模型对混合噪声的去除效果及空谱维的光滑性。此外,文中采用约束的平滑函数来近似秩函数,以更好地利用HSI的低秩属性并提高了算法效率。该优化问题采用基于交替方向乘子的迭代算法进行求解。两种加噪情况的模拟数据实验和一项基于真实数据的实验的结果表明,相比5种目前主流的方法,所提方法在目视效果和评价指标上都有明显提升。
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