  A mainstream type of the state of the arts (SOTAs) based on convolutional
neural network (CNN) for real image denoising contains two sub-problems, i.e.,
noise estimation and non-blind denoising. This paper considers real noise
approximated by heteroscedastic Gaussian/Poisson Gaussian distributions with
in-camera signal processing pipelines. The related works always exploit the
estimated noise prior via channel-wise concatenation followed by a
convolutional layer with spatially sharing kernels. Due to the variable modes
of noise strength and frequency details of all feature positions, this design
cannot adaptively tune the corresponding denoising patterns. To address this
problem, we propose a novel conditional filter in which the optimal kernels for
different feature positions can be adaptively inferred by local features from
the image and the noise map. Also, we bring the thought that alternatively
performs noise estimation and non-blind denoising into CNN structure, which
continuously updates noise prior to guide the iterative feature denoising. In
addition, according to the property of heteroscedastic Gaussian distribution, a
novel affine transform block is designed to predict the stationary noise
component and the signal-dependent noise component. Compared with SOTAs,
extensive experiments are conducted on five synthetic datasets and three real
datasets, which shows the improvement of the proposed CFNet.
