  Despite their recent success on image denoising, the need for deep and
complex architectures still hinders the practical usage of CNNs. Older but
computationally more efficient methods such as BM3D remain a popular choice,
especially in resource-constrained scenarios. In this study, we aim to find out
whether compact neural networks can learn to produce competitive results as
compared to BM3D for AWGN image denoising. To this end, we configure networks
with only two hidden layers and employ different neuron models and layer widths
for comparing the performance with BM3D across different AWGN noise levels. Our
results conclusively show that the recently proposed self-organized variant of
operational neural networks based on a generative neuron model (Self-ONNs) is
not only a better choice as compared to CNNs, but also provide competitive
results as compared to BM3D and even significantly surpass it for high noise
levels.
