hsimatlab代码-QRNN3D:用于高光谱图像去噪的3D拟递归神经网络(TNNLS2020)

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hsi matlab代码QRNN3D TNNLS 2020论文的实施 强调 我们的网络在高斯和复杂噪声情况下均优于ICVL数据集上的所有领先方法(2019),如下所示: 我们证明了在31频段自然HSI数据库(ICVL)上进行预训练的网络可用于恢复由于恶劣的大气和水吸收而被现实世界的非高斯噪声破坏的遥感HSI(> 100频段) 先决条件 Python> = 3.5,PyTorch> = 0.4.1 要求:opencv-python,tensorboardX,caffe 平台:Ubuntu 16.04,cuda-8.0 快速开始 1.准备训练/测试数据集 从以下位置下载ICVL高光谱图像数据库(我们仅需要.mat版本) 火车测试拆分可在ICVL_train.txt和ICVL_test_*.txt 。 (请注意,我们分别将101个测试数据分为高斯和复数降噪两部分。) 训练数据集 注意cafe(通过conda安装)和lmdb是执行以下说明所必需的。 阅读utility/lmdb_data.py的函数create_icvl64_31 ,并按照指令注释定义您的数据/数据集地址。 通过python

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