Image denoising by Super Neurons: Why go deep?

11/29/2021
by   Junaid Malik, et al.
37

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.

READ FULL TEXT

page 4

page 6

page 7

page 8

page 9

03/04/2021

Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising

Real-world blind denoising poses a unique image restoration challenge du...
07/19/2019

Deep Graph-Convolutional Image Denoising

Non-local self-similarity is well-known to be an effective prior for the...
03/04/2021

BM3D vs 2-Layer ONN

Despite their recent success on image denoising, the need for deep and c...
09/01/2020

Operational vs Convolutional Neural Networks for Image Denoising

Convolutional Neural Networks (CNNs) have recently become a favored tech...
08/29/2020

Self-Organized Operational Neural Networks for Severe Image Restoration Problems

Discriminative learning based on convolutional neural networks (CNNs) ai...
11/07/2017

Neural system identification for large populations separating "what" and "where"

Neuroscientists classify neurons into different types that perform simil...
08/31/2018

An Adaptive Locally Connected Neuron Model: Focusing Neuron

We present a new artificial neuron model capable of learning its recepti...