Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: When AWGN-based Denoiser Meets Real Noises   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
https://github.com/yzhouas/PD-Denoising-pytorch.

17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn