deep learning models trained to denoise/deblur text images (signle frame, multi-frame) [pytorch]
benchmark dataset: LP HDR dataset
- s&p + gaussian blur
- single frame
- real noise map
deep learning models trained to denoise/deblur text images (signle frame, multi-frame) [pytorch]
License: MIT License
deep learning models trained to denoise/deblur text images (signle frame, multi-frame) [pytorch]
benchmark dataset: LP HDR dataset
Hi, do you have pre-trained data that I can use on the fly?
The given link for the pre-trained model is not working can you please provide the new one
Hi, something interesting here:
https://github.com/zabir-nabil/text-image-denoiser/blob/master/infer_demo.py
When we tried to use your code, we noticed that you plot y. I guess it should be output, right?
cnt = 0
t = 0
for i, data in enumerate(dataloader, 0):
# get the inputs; data is a list of [inputs, labels]
X, y = data
t1 = time.time()
X = X.cuda()
y = y.cuda()
outputs = model(X)
t2 = time.time()
t += (t2-t1)
plot_save(X[0,:,:,:].cpu(), **y[0,:,:,:].cpu()**, 'unet_demo/', cnt)
cnt += 1
if cnt == 100:
break
print(f'Time average: {t/100.}')
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