Comments (7)
okay .. and thanks for the code
from esrgan-tf2.
can u please tell me the reason, though esrgan has good results visually, but the psnr is low as compaired to psnr results
from esrgan-tf2.
Hey, @nan-rock. Higher PSNR dose not mean better visual quality but fidelity.
from esrgan-tf2.
hey,i need your help. when i am using imresize_np() to generte set 14 low resolution images, my images are getting distorted.
this is my code
`path="D:/SR_testing_datasets/Set14"
savepath="d:"
for img_name in os.listdir(path):
raw_img = cv2.imread(os.path.join(path, img_name))
shape=raw_img.shape
#print(shape)
new_image=imresize_np(raw_img, (2)).astype(np.uint8)
#print("new",new_image.shape)
#new_image=tf.image.resize( raw_img, [shape[0]+1,shape[1]+1], method ='bilinear'4
result_img_path = os.path.join(savepath,img_name)
#print( result_img_path)
#print(result_img_path)
#print(os.listdir(result_img_path))
a=cv2.imwrite(result_img_path, new_image)
print(a) `
from esrgan-tf2.
Try np.clip() value to 0 - 255 before astype to uint8
from esrgan-tf2.
thanks a lot sir.
wish u good luck.
from esrgan-tf2.
hello sir, is the result evaluated on y channel or RGB
from esrgan-tf2.
Related Issues (15)
- Why the training effect of using your own data set is very poor HOT 4
- Esrgan_training D_loss=0
- Use the LR_test image to test
- loss_maybe_wrong
- Speed
- output SR images saved in comparison mode.
- NOT an issue: It works amazingly well. However, use Spectral Normalization will converge VERY fast and nice ! HOT 1
- Quality
- Transfer learning
- Loss is always nan HOT 1
- update the gradient do not need freeze one of network? HOT 2
- building tfreacords file: HOT 1
- psnr traning_processing was stoped 'by itself' when step to 200000
- How long did you training to get the pre_train model ckpt-500?
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