  Deep image denoising models often rely on large amount of training data for
the high quality performance. However, it is challenging to obtain sufficient
amount of data under real-world scenarios for the supervised training. As such,
synthesizing realistic noise becomes an important solution. However, existing
techniques have limitations in modeling complex noise distributions, resulting
in residual noise and edge artifacts in denoising methods relying on synthetic
data. To overcome these challenges, we propose a novel method that synthesizes
realistic noise using diffusion models, namely Realistic Noise Synthesize
Diffusor (RNSD). In particular, the proposed time-aware controlling module can
simulate various environmental conditions under given camera settings. RNSD can
incorporate guided multiscale content, such that more realistic noise with
spatial correlations can be generated at multiple frequencies. In addition, we
construct an inversion mechanism to predict the unknown camera setting, which
enables the extension of RNSD to datasets without setting information.
Extensive experiments demonstrate that our RNSD method significantly
outperforms the existing methods not only in the synthesized noise under
multiple realism metrics, but also in the single image denoising performances.
