Noisy-As-Clean

Install

To reproduce the results in the NAC paper:

 python nac_resnet_on_set12.py

Pre-trained models for AWGN denoising on Set12 are provided in the results folder

Modify the specific settings that need to test in test_nac_resnet_on_set12.py

 run test_nac_resnet_on_set12.py

#Further comments: The code is mainly written by Yuan Huang.

Part of the code borrow from [Deep Image Prior]

Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
SomeISP_operator_python		SomeISP_operator_python
data		data
models		models
utils		utils
README.md		README.md
nac_dncnn_bsd68.py		nac_dncnn_bsd68.py
nac_dncnn_bsd68_blind.py		nac_dncnn_bsd68_blind.py
nac_dncnn_set12.py		nac_dncnn_set12.py
nac_dncnn_set12_blind.py		nac_dncnn_set12_blind.py
nac_mydncnn_bsd68.py		nac_mydncnn_bsd68.py
nac_mydncnn_bsd68_blind.py		nac_mydncnn_bsd68_blind.py
nac_mydncnn_set12.py		nac_mydncnn_set12.py
nac_mydncnn_set12_blind.py		nac_mydncnn_set12_blind.py
nac_resnet_bsd68.py		nac_resnet_bsd68.py
nac_resnet_bsd68_blind.py		nac_resnet_bsd68_blind.py
nac_resnet_set12.py		nac_resnet_set12.py
nac_resnet_set12_blind.py		nac_resnet_set12_blind.py
pipeline_denoising_withaug_post_square_input.py		pipeline_denoising_withaug_post_square_input.py
pipeline_s2s_cc_square_mydncnn.py		pipeline_s2s_cc_square_mydncnn.py
pipeline_s2s_cc_square_resnet.py		pipeline_s2s_cc_square_resnet.py
test_nac_resnet_on_set12.py		test_nac_resnet_on_set12.py