Super Resolution - OpenCV.ipynb

Kernel: Python 3

Import Modules

import cv2
from cv2 import dnn_superres

# links to download models
# https://github.com/Saafke/EDSR_Tensorflow/blob/master/models/
# https://github.com/fannymonori/TF-LapSRN/blob/master/export/LapSRN_x8.pb

EDSR

# initialize super resolution object
sr = dnn_superres.DnnSuperResImpl_create()

# read the model
path = 'EDSR_x4.pb'
sr.readModel(path)

# set the model and scale
sr.setModel('edsr', 4)

# if you have cuda support
sr.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
sr.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

# load the image
image = cv2.imread('test.png')

# upsample the image
upscaled = sr.upsample(image)
# save the upscaled image
cv2.imwrite('upscaled_test.png', upscaled)

True

# traditional method - bicubic
bicubic = cv2.resize(image, (upscaled.shape[1], upscaled.shape[0]), interpolation=cv2.INTER_CUBIC)
# save the image
cv2.imwrite('bicubic_test.png', bicubic)

True

LapSRN

# initialize super resolution object
sr = dnn_superres.DnnSuperResImpl_create()

# read the model
path = 'LapSRN_x8.pb'
sr.readModel(path)

# set the model and scale
sr.setModel('lapsrn', 8)

# if you have cuda support
sr.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
sr.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

# load the image
image = cv2.imread('test.png')

# upsample the image
upscaled = sr.upsample(image)
# save the upscaled image
cv2.imwrite('upscaled_test_lapsrn.png', upscaled)

True

# traditional method - bicubic
bicubic = cv2.resize(image, (upscaled.shape[1], upscaled.shape[0]), interpolation=cv2.INTER_CUBIC)
# save the image
cv2.imwrite('bicubic_test_8x.png', bicubic)

True