计算机科学 ›› 2024, Vol. 51 ›› Issue (5): 151-161.doi: 10.11896/jsjkx.230200044
姜斌, 叶军, 张历洪, 司伟纳
JIANG Bin, YE Jun, ZHANG Lihong, SI Weina
摘要： 高光谱图像(HSI)具有良好的光谱识别能力,被广泛地应用于各种领域。然而,HSI在成像过程中易受到混合噪声的污染,会严重削弱后续任务的准确性,如何高质量地恢复HSI是需要解决的首要问题。目前,基于低秩先验和全变分正则化结合的HSI去噪方法取得了较好的性能,但这些方法一方面忽略了高强度条纹噪声在空间结构和光谱分布上的特征,使得噪声无法完全去除,另一方面没有考虑HSI差分图像低秩子空间的信息,不能挖掘潜在的局部空间光滑结构。为此,提出了一种基于双平滑函数秩近似和群稀疏的HSI恢复模型。首先,利用双平滑函数秩近似模型探索干净HSI和条纹噪声的低秩结构,去除结构化条纹噪声等高强度混合噪声。其次,将基于E3DTV的群稀疏正则化融入双平滑函数秩近似模型中,充分挖掘HSI差分图像的稀疏先验信息,进一步提升图像在空间恢复和光谱特征保留方面的性能。最后,设计了交替方向乘子法(ADMM)求解所提出的BSRAGS模型。仿真和真实数据实验均表明,所提模型能够有效提高图像恢复质量。
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