  Real-world blind denoising poses a unique image restoration challenge due to
the non-deterministic nature of the underlying noise distribution. Prevalent
discriminative networks trained on synthetic noise models have been shown to
generalize poorly to real-world noisy images. While curating real-world noisy
images and improving ground truth estimation procedures remain key points of
interest, a potential research direction is to explore extensions to the widely
used convolutional neuron model to enable better generalization with fewer data
and lower network complexity, as opposed to simply using deeper Convolutional
Neural Networks (CNNs). Operational Neural Networks (ONNs) and their recent
variant, Self-organized ONNs (Self-ONNs), propose to embed enhanced
non-linearity into the neuron model and have been shown to outperform CNNs
across a variety of regression tasks. However, all such comparisons have been
made for compact networks and the efficacy of deploying operational layers as a
drop-in replacement for convolutional layers in contemporary deep architectures
remains to be seen. In this work, we tackle the real-world blind image
denoising problem by employing, for the first time, a deep Self-ONN. Extensive
quantitative and qualitative evaluations spanning multiple metrics and four
high-resolution real-world noisy image datasets against the state-of-the-art
deep CNN network, DnCNN, reveal that deep Self-ONNs consistently achieve
superior results with performance gains of up to 1.76dB in PSNR. Furthermore,
Self-ONNs with half and even quarter the number of layers that require only a
fraction of computational resources as that of DnCNN can still achieve similar
or better results compared to the state-of-the-art.
