自适应高光谱图像去高脉冲噪声的校正方法
首发时间:2019-01-22
摘要:针对高光谱图像(HSI)中高强度脉冲噪声的去除问题,本文提出了基于张量表示和TV正则化的自适应校正模型.自适应校正的过程分为两步:第一步是通过求解LRTDTV模型生成初值;第二步即校正步,校正步可能会重复多次,其目的是进一步提高L1-范数拟合项的稀疏性.最后对六种模拟噪声数据进行数值实验,实验结果体现了本文所提方法的优越性,尤其是针对高水平椒盐噪声的去除.
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Adaptive correction procedure for Hyperspectral image deblurring under heavy impulse noise
Abstract:This paper studies the Hyperspectral image (HSI) denoising problem under the assumption that the signal is corrupted by heavy impulse noise. In order to removal the high level impulse noise, we propose an adaptive correction procedure for LRTDTV model based on tensor representation and TV regularization. The process of adaptive correction is as follows: the first step is to generate the initial value by solving the LRTDTV model; The second step is the correction step, which may be repeated many times. The correction step is to correct for sparsity of the L1-norm data term. Six kinds of experiment on simulated noise HSI data are carried out to demonstrate the superiority of the proposed method, especially for the removal of high level pepper and salt and pepper noise.
Keywords: Image Procssing Hyperspectral image low rank sparsity Total Variation impulse noise
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