question about the network for flash guided non-flash denoising task
Opened this issue · 1 comments
JingyiXu404 commented
The inputs for RGB-NIR task are 'noisy_img'(h,w,3), 'guidance_img'(h,w,1) and you split the RGB noisy input images following:
with torch.no_grad():
denoised_r, _, _ = model(noisy_img[:,0,:,:,].unsqueeze(0), guidance_img)
denoised_g, _, _ = model(noisy_img[:,1,:,:,].unsqueeze(0), guidance_img)
denoised_b, _, _ = model(noisy_img[:,2,:,:,].unsqueeze(0), guidance_img)
denoised_img = torch.cat([denoised_r, denoised_g, denoised_b], dim=1)
But in flash guided non-flash denoising task, both input images have 3 channels:
'noisy_img'(h,w,3), 'guidance_img'(h,w,3)
Could you tell me what modification should be done in 'FGDNet.py' if I want to train the model for flash guided non-flash denoising task?
Or can I just keep 'FGDNet.py' the same and split the inputs following:
with torch.no_grad():
denoised_r, _, _ = model(noisy_img[:,0,:,:,].unsqueeze(0), guidance_img[:,0,:,:,].unsqueeze(0))
denoised_g, _, _ = model(noisy_img[:,1,:,:,].unsqueeze(0), guidance_img[:,1,:,:,].unsqueeze(0))
denoised_b, _, _ = model(noisy_img[:,2,:,:,].unsqueeze(0), guidance_img[:,2,:,:,].unsqueeze(0))
denoised_img = torch.cat([denoised_r, denoised_g, denoised_b], dim=1)
Thanks a lot!
lustrouselixir commented
@JingyiXu404 Hi! Thanks for your attention. FGDNet processes one channel at a time for both target and guidance images, as in your second codes.