Discriminative learning-based image denoisers have achieved promising
performance on synthetic noises such as Additive White Gaussian Noise (AWGN).
The synthetic noises adopted in most previous work are pixel-independent, but
real noises are mostly spatially/channel-correlated and
spatially/channel-variant. This domain gap yields unsatisfied performance on
images with real noises if the model is only trained with AWGN. In this paper,
we propose a novel approach to boost the performance of a real image denoiser
which is trained only with synthetic pixel-independent noise data dominated by
AWGN. First, we train a deep model that consists of a noise estimator and a
denoiser with mixed AWGN and Random Value Impulse Noise (RVIN). We then
investigate Pixel-shuffle Down-sampling (PD) strategy to adapt the trained
model to real noises. Extensive experiments demonstrate the effectiveness and
generalization of the proposed approach. Notably, our method achieves
state-of-the-art performance on real sRGB images in the DND benchmark among
models trained with synthetic noises. Codes are available at
this https URL

本文提出了一种使用 Pixel-shuffle Down-sampling 策略的新方法，该方法通过训练一个深度模型（由噪声估计器和去噪器组成）使用混合的 AWGN 和 RVIN 数据，从而提高使用合成独立像素噪声数据训练的实际图像去噪器返回结果的效果和泛化性能，并在 DND 基准测试的实际 sRGB 图像中取得了最先进的性能。