Operational vs Convolutional Neural Networks for Image Denoising

• URL: http://arxiv.org/abs/2009.00612v1
• Date: Tue, 1 Sep 2020 12:15:28 GMT
• Title: Operational vs Convolutional Neural Networks for Image Denoising
• Authors: Junaid Malik, Serkan Kiranyaz, Moncef Gabbouj
• Abstract summary: Convolutional Neural Networks (CNNs) have recently become a favored technique for image denoising due to its adaptive learning ability. We propose a heterogeneous network model which allows greater flexibility for embedding additional non-linearity at the core of the data transformation. An extensive set of comparative evaluations of ONNs and CNNs over two severe image denoising problems yield conclusive evidence that ONNs enriched by non-linear operators can achieve a superior denoising performance against CNNs with both equivalent and well-known deep configurations.
• Score: 25.838282412957675
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Convolutional Neural Networks (CNNs) have recently become a favored technique for image denoising due to its adaptive learning ability, especially with a deep configuration. However, their efficacy is inherently limited owing to their homogenous network formation with the unique use of linear convolution. In this study, we propose a heterogeneous network model which allows greater flexibility for embedding additional non-linearity at the core of the data transformation. To this end, we propose the idea of an operational neuron or Operational Neural Networks (ONN), which enables a flexible non-linear and heterogeneous configuration employing both inter and intra-layer neuronal diversity. Furthermore, we propose a robust operator search strategy inspired by the Hebbian theory, called the Synaptic Plasticity Monitoring (SPM) which can make data-driven choices for non-linearities in any architecture. An extensive set of comparative evaluations of ONNs and CNNs over two severe image denoising problems yield conclusive evidence that ONNs enriched by non-linear operators can achieve a superior denoising performance against CNNs with both equivalent and well-known deep configurations.

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