Thanks for your sharing.
I see there is not residual structure in the AdaFM module.
Why it returns self.transformer(x) + x instead of self.transformer(x) ?
Thanks.
Hi,i have one doubt about the paper and the corresponding code. in the paper ,fmid = f15 + lambda(g - I) * f15; but I think the code corresponds fmid = f15 + lambda*g * f15 ?I cannot fully understanding this .
Hi, you have done a great job!
Could you please share the supplemental material?
It is highly apprepriated if you can also share the PSNR distances of Modulation Testing (Sec. 4.3) on denoising and super resolution tasks. The performances look really good on the DeJEPG task.
There are some problem on the subtraction of two images. how can I avoid it?
mse = np.mean((img1 - img2)**2)
ValueError: operands could not be broadcast together with shapes (330,502,3) (1348,2032,3)
It's basically in this position: "util.py", line 111
According to your original "basic.json" file ,if i have a LR dataset with 120120 pixels images,and a HR dataset with 480480 pixels images; I need to modify the "crop_size" parameter from 0 to 4; but i find this is wrong ; i can't get any message from feeddata with same size 96*96 ; so how can i make it right?