  Classical image denoising methods utilize the non-local self-similarity
principle to effectively recover image content from noisy images. Current
state-of-the-art methods use deep convolutional neural networks (CNNs) to
effectively learn the mapping from noisy to clean images. Deep denoising CNNs
manifest a high learning capacity and integrate non-local information owing to
the large receptive field yielded by numerous cascade of hidden layers.
However, deep networks are also computationally complex and require large data
for training. To address these issues, this study draws the focus on the
Self-organized Operational Neural Networks (Self-ONNs) empowered by a novel
neuron model that can achieve a similar or better denoising performance with a
compact and shallow model. Recently, the concept of super-neurons has been
introduced which augment the non-linear transformations of generative neurons
by utilizing non-localized kernel locations for an enhanced receptive field
size. This is the key accomplishment which renders the need for a deep network
configuration. As the integration of non-local information is known to benefit
denoising, in this work we investigate the use of super neurons for both
synthetic and real-world image denoising. We also discuss the practical issues
in implementing the super neuron model on GPUs and propose a trade-off between
the heterogeneity of non-localized operations and computational complexity. Our
results demonstrate that with the same width and depth, Self-ONNs with super
neurons provide a significant boost of denoising performance over the networks
with generative and convolutional neurons for both denoising tasks. Moreover,
results demonstrate that Self-ONNs with super neurons can achieve a competitive
and superior synthetic denoising performances than well-known deep CNN
denoisers for synthetic and real-world denoising, respectively.
