The RGB images have three color channels (24-bit image), but the grayscale image had only a single grayscale image (8-bit image). In our previous experiment, we normalized the images of the pre- trained network by each color channel separately. This approach lost the information provided by the other two channels. Here, we used the same grayscale LDCT image for each channel to make it somewhat analogous to an RGB image for the CNN. Doing so engages all the weights and exploits all the learned knowledge when extracting features from the pretrained net- work. Since the images experimented with were smaller than the required input size of the pretrained CNN (224 × 224), we used bicubic interpolation for resizing. The dimension of a deep feature vector extracted for each image was 4096. The features were the output of the last fully connected layer (the full 2 layer as shown in Table 2) before the output layer in an ImageNet pretrained CNN. The architectures and para 解释

这段话主要是关于一个实验中的图像处理和深度学习网络的使用。该实验中使用了一个预训练的卷积神经网络（CNN）来提取图像的特征，以用于后续的任务。由于原始图像是RGB图像，而预训练网络是对彩色图像进行训练的，因此将灰度图像复制三份，以便处理成类似于RGB图像的形式，以便充分利用预训练网络的知识。因为图像的大小比预训练网络的输入大小要小，所以使用了双三次插值的方法进行图像的缩放。最后，提取了每个图像的深度特征向量，其维度为4096，这些特征是在ImageNet预训练CNN的输出层之前的最后一个全连接层（即表2中的完整2层）的输出结果。