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Demo_DnCNNstar_Random.py
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Demo_DnCNNstar_Random.py
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from DataFidelities.RandomClass import RandomClass
from Regularizers.robjects_tf import *
from iterAlgs import *
from util import *
import scipy.io as sio
import numpy as np
import os
####################################################
#### HYPER-PARAMETERS ###
####################################################
# indicate the GPU index if available. If not, just leave it
gpu_ind = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_ind;
# set the random seed, please do not comment this line
np.random.seed(128)
## optimal tau values for residual DnCNN with Random Matrix with 30 dB input SNR noise
DnCNN_taus_30dB = [0.3947665428470804, 0.33916115451527434, 0.31724239698349554, 0.39744749839135796,
0.2556451911121183, 0.4109487505623313, 0.6721322329761701, 0.322682764758653,
0.386136952197389, 0.3410113490614658]
## optimal tau values for residual DnCNN with Random Matrix with 40 dB input SNR noise
DnCNN_taus_40dB = [0.20051761636796708, 0.23342255207802717, 0.25608225198595563, 0.20984552245851243,
0.1944676571465673, 0.355732263964717, 0.28946240847511034, 0.28102085922777414,
0.3154775141742967, 0.2559106590509435]
# you can change the save path here
save_root = 'results/Demo_DnCNNstar_Random/'
# allocating folders
abs_save_path = os.path.abspath(save_root)
if os.path.exists(save_root):
print("Removing '{:}'".format(abs_save_path))
shutil.rmtree(abs_save_path, ignore_errors=True)
# make new path
print("Allocating '{:}'".format(abs_save_path))
os.makedirs(abs_save_path)
####################################################
#### DATA PREPARATION ###
####################################################
# here we load all 10 test images
data_name = 'Knee_10'
data = sio.loadmat('data/{}.mat'.format(data_name), squeeze_me=True)
imgs = np.squeeze(data['img'])
# prepare for the data info
sigSize = np.array(imgs[...,0].shape)
num_blocks = 16
block_size = 40
downsample_rate = 0.71 # 0.71*0.71 = 0.5
noiseLevel = 40 # two noise levels {30,40} are available for the simulation.
# generate random measurement matrix used for all 10 images.
print()
print('Generating random measurement matrix . . .')
A, A_patches = RandomClass.genMeas(sigSize, num_blocks, block_size, downsample_rate=downsample_rate)
print('. . . Done')
print()
# number of iterations
iters = 100
####################################################
#### NETWORK INITIALIZATION ###
####################################################
#-- Network Hyperparameters --#
input_channels = 1
truth_channels = 1
#-- Network Setup --#
# select the DnCNNstar model
# - Please use 'residual_DnCNNstar_LC=2/DnCNN_layer=7_sigma=5' to generate the optimal results for DnCNNstar.
model_name = 'DnCNN_layer=7_sigma=5'
model_path = 'models/residual_DnCNNstar_LC=2/{}/model.cpkt'.format(model_name)
####################################################
#### LOOP IMAGES ###
####################################################
numImgs = imgs.shape[2]
bcred_output = {}
red_output = {}
bcred_dist = np.zeros(iters)
red_dist = np.zeros(iters)
bcred_snr = np.zeros(iters)
red_snr = np.zeros(iters)
# select which image you want to reconstruct. By default we use the sixth image.
startIndex = 5
endIndex = 6
for i in range(startIndex,endIndex):
# extract truth
x = imgs[...,i]
xtrue = x
sigSize = np.array(x.shape)
# measure
y = RandomClass.fmult(x, A)
# add white gaussian noise
y,_ = util.addwgn(y, noiseLevel)
####################################################
#### DnCNN ###
####################################################
tau = DnCNN_taus_40dB[i]
#-- Reconstruction --#
dObj = RandomClass(y, sigSize, A, A_patches)
rObj = DnCNNClass(sigSize, tau, model_path, img_channels=input_channels, truth_channels=truth_channels)
# rObj = TVClass(sigSize, 0.1, 0.001, maxiter=20) # Qualitative analysis, parameters not optimized
print()
print('#######################')
print('#### BCRED (epoch) ####')
print('#######################')
print()
# - To try out direct DnCNN, set useNoise to False.
# - To denoise with full denoiser, set pad to None.
# - To denoise with block-wise denoiser, set pad to some scalar (5 by default).
# We set the step-size to be 1/(L+2*tau)
bcred_recon, bcred_out = bcredEst(dObj, rObj,
num_patch=num_blocks, patch_size=block_size, pad=5, numIter=iters, step=1/(2+2*tau),
useNoise=True, verbose=True, xtrue=xtrue)
bcred_out['recon'] = bcred_recon
print()
print('###################')
print('#### RED ####')
print('###################')
print()
# - To try out direct DnCNN, set useNoise to False.
# - To save intermediate results, set is_save to True.
red_recon, red_out = redEst(dObj, rObj,
numIter=iters, step=1/(6+2*tau), accelerate=False, mode='RED', useNoise=True,
verbose=True, xtrue=xtrue) # set useNoise to False if you want to try out direct DnCNN
red_out['recon'] = red_recon
# save out info
bcred_output['img_{}'.format(i)] = bcred_out
red_output['img_{}'.format(i)] = red_out
sio.savemat(save_root + 'bcred_out.mat', bcred_output)
sio.savemat(save_root + 'red_out.mat', red_output)
bcred_dist = bcred_dist + np.array(bcred_out['dist'])
red_dist = red_dist + np.array(red_out['dist'])
bcred_snr = bcred_snr + np.array(bcred_out['snr'])
red_snr = red_snr + np.array(red_out['snr'])
####################################################
#### PlOTTING CONVERGENCE ###
####################################################
import matplotlib.pyplot as plt
num = endIndex - startIndex
# compute the averaged distance to fixed points
avgDistBcred = np.squeeze(bcred_dist / num)
avgDistRed = np.squeeze(red_dist / num)
avgSnrBcred = np.squeeze(bcred_snr / num)
avgSnrRed = np.squeeze(red_snr / num)
xRange = np.linspace(0,iters,iters)
fig, (ax1, ax2) = plt.subplots(1, 2)
# Convergence Plot
ax1.semilogy(xRange, avgDistBcred, label='BC-RED (epoch)')
ax1.semilogy(xRange, avgDistRed, label='RED')
ax1.set_xlim(0,iters)
ax1.set_ylim(1e-7,1)
ax1.set_xlabel('iteration')
ax1.set_ylabel('accuracy')
ax1.set_title('Convergence plot for BC-RED and RED')
plt.legend()
# SNR Plot
ax2.plot(xRange, avgSnrBcred, label='BC-RED (epoch)')
ax2.plot(xRange, avgSnrRed, label='RED')
ax2.set_xlim(0,iters)
ax2.set_ylim(0,30)
ax2.set_xlabel('iteration')
ax2.set_ylabel('SNR (dB)')
ax2.set_title('SNR plot for BC-RED and RED')
plt.legend()
plt.show()