This code implements the neural proximal gradient descent (PGD) algorithm proposed in https://arxiv.org/abs/1806.03963. The idea is to unroll the proximal gradient descent algorithm and model the proximal using a neural network. Adopting residual network (ResNet) as the proximal, a recurrent neural net (RNN) is implemented to learn the proximal. The code is flexible to incorporate a combination of various training costs including: 1) pixel-wise l1/l2, 2) SSIM, and 3) adversarial GAN, LSGAN, and WGAN.
For medical image reconstruction we adopt the MRI datasets available at the https://www.mridata.org made available as a result of a joint collaboration between Stanford & UC Berkeley. It includes a 20 3D Knee images that have a high resoltuion of 320x320x256. 320 2D axial slices are collected from all patients to form the training and test datasets.
-- The input files have .jpg format in the train and test folders
-- The sampling mask is randomly generated based on a avariable density with radial view ordering sampling technique. The Matlab code is avialble at http://mrsrl.stanford.edu/~jycheng/software.html
CelebA Face dataset
Adopting celebFaces Attributes Dataset (CelebA) for train and test we use 10K and 1,280 images, respectively. Ground-truth images has 128×128 pixels that is downsampled to 32×32 LR images.