Super Image Resolution By Artificial Intelligence Deep Learning

 Artificial Intelligence Super Image Resolution

Upscale From 565 x 559 To 2260 x 2236

  

Super Resolution Image By Deep Learning Library EDSR_x4 :-

import cv2

from cv2 import dnn_superres

# 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('../MessageMP3/lowimg.jpg')

# upsample the image

upscaled = sr.upsample(image)

# save the upscaled image

cv2.imwrite('../MessageMP3/high.jpg', upscaled)

# traditional method - bicubic

bicubic = cv2.resize(image, (upscaled.shape[1], upscaled.shape[0]), interpolation=cv2.INTER_CUBIC)

# save the image

cv2.imwrite('../MessageMP3/highbicube.jpg', bicubic)

 

Super Resolution Image By Deep Learning Library LapSRN_x8 :-

import cv2

from cv2 import dnn_superres

# 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('MessageMP3/lowimg.jpg')

# upsample the image

upscaled = sr.upsample(image)

# save the upscaled image

cv2.imwrite('MessageMP3/high.jpg', upscaled)

# traditional method - bicubic

bicubic = cv2.resize(image, (upscaled.shape[1], upscaled.shape[0]), interpolation=cv2.INTER_CUBIC)

# save the image

cv2.imwrite('MessageMP3/highbicube.jpg', bicubic)

Super Resolution Image By Deep Learning Library FSRCNN_x3 :-

import cv2

import matplotlib.pyplot as plt

img = cv2.imread("../MessageMP3/lowimg.jpg")

sr = cv2.dnn_superres.DnnSuperResImpl_create()

path = "FSRCNN_x3.pb"

sr.readModel(path)

sr.setModel("fsrcnn",3)

result = sr.upsample(img)

cv2.imwrite("../MessageMP3/highimg1.jpg",result)

Super Resolution Image By Deep Learning Library FSRCNN_x4 :-

import cv2

import matplotlib.pyplot as plt

img = cv2.imread("../MessageMP3/lowimg.jpg")

sr = cv2.dnn_superres.DnnSuperResImpl_create()

path = "FSRCNN_x4.pb"

sr.readModel(path)

sr.setModel("fsrcnn",4)

result = sr.upsample(img)

cv2.imwrite("../MessageMP3/highimg1.jpg",result)