Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization (Accepted for CVPR 2020)

Yoonsik Kim, Jae Woong Soh, Gu Yong Park, and Nam Ik Cho

[Arxiv]

Environments

Ubuntu 16.04
Tensorflow 1.8
CUDA 9.0 & cuDNN 7.1
Python 3.6

Test Code

Code

Trained Model

Abstract

Real-noise denoising is a challenging task because the statistics of real-noise do not follow the normal distribution, and they are also spatially and temporally changing. In order to cope with various and complex real-noise, we propose a well-generalized denoising architecture and a transfer learning scheme. Specifically, we adopt an adaptive instance normalization to build a denoiser, which can regularize the feature map and prevent the network from overfitting to the training set. We also introduce a transfer learning scheme that transfers knowledge learned from synthetic-noise data to the real-noise denoiser. From the proposed transfer learning, the synthetic-noise denoiser can learn general features from various synthetic-noise data, and the real-noise denoiser can learn the real-noise characteristics from real data. From the experiments, we find that the proposed denoising method has great generalization ability, such that our network trained with synthetic-noise achieves the best performance for Darmstadt Noise Dataset (DND) among the methods from published papers. We can also see that the proposed transfer learning scheme robustly works for real-noise images through the learning with a very small number of labeled data.

Brief Description of Proposed Method

Adaptive Instance Normalization Denoising Network

We propose a novel well-generalized denoiser based on the AIN, which enables the CNN to work for various noise from many camera devices.