I am trying to use the 3D convolution neural nets to do some Deconvolution task.(i.e. task like 3D deblur)

The image sequences are of size 101*101*101

The data are fake 3D tree shape blood vessel models. I use the code from http://vascusynth.cs.sfu.ca/Data.html 3 to generate 1500 volumn models, each of which is of size 101*101*101. I convolve them with a 10*10*10 kernel to simulate the psf in the imaging process and add 30dB psnr noise. But now I just feed the same data into the net as the targeted output. I originally hoped to use 1200 of them as training set and the rest as the test set.
Batch size is four(larger size like 8 would cause cuda out of memory error)
I am not normalizing it currently, I will first try with that next.
Thank you so much for helping me!