  During the image acquisition process, noise is usually added to the data
mainly due to physical limitations of the acquisition sensor, and also
regarding imprecisions during the data transmission and manipulation. In that
sense, the resultant image needs to be processed to attenuate its noise without
losing details. Non-learning-based strategies such as filter-based and noise
prior modeling have been adopted to solve the image denoising problem.
Nowadays, learning-based denoising techniques showed to be much more effective
and flexible approaches, such as Residual Convolutional Neural Networks. Here,
we propose a new learning-based non-blind denoising technique named Attention
Residual Convolutional Neural Network (ARCNN), and its extension to blind
denoising named Flexible Attention Residual Convolutional Neural Network
(FARCNN). The proposed methods try to learn the underlying noise expectation
using an Attention-Residual mechanism. Experiments on public datasets corrupted
by different levels of Gaussian and Poisson noise support the effectiveness of
the proposed approaches against some state-of-the-art image denoising methods.
ARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for
Gaussian and Poisson denoising, respectively FARCNN presented very consistent
results, even with slightly worsen performance compared to ARCNN.
