Q-AE
This code is about paper: "Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising" (https://ieeexplore.ieee.org/document/8946589), An autoencoder based on new types of neurons There are 5 quadratic convolutional layers and 5 quadratic deconvolutional layers in Q-AE, where each layer has 15 quadratic filters of 5×5, followed by a ReLU layer. Zero paddings are used in the first four layer, therefore the fifth layer is the bottleneck layer.
The core part of this code is to define "quadratic convolution":
tf.nn.relu((conv2d_valid(input, W_r)+b_r)(conv2d_valid(input, W_g)+b_g)+conv2d_valid(inputinput, W_b)+c)