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Self-Organized Operational Neural Networks for Severe Image Restoration Problems by Malik, Junaid; Kiranyaz, Serkan; Gabbouj, Moncef is a scholarly article available to read on EtoBox.

What is Self-Organized Operational Neural Networks for Severe Image Restoration Problems about?

Discriminative learning based on convolutional neural networks (CNNs) aims to perform image restoration by learning from training examples of noisy-clean image pairs. It has become the go-to methodology for tackling image restoration and has outperformed the traditional non-local class of methods. However, the top-performing networks are generally composed of many convolutional layers and hundreds of neurons, with trainable parameters in excess of several millions. We claim that this is due to the inherent linear nature of convolution-based transformation, which is inadequate for handling severe restoration problems. Recently, a non-linear generalization of CNNs, called the operational neural networks (ONN), has been shown to outperform CNN on AWGN denoising. However, its formulation is burdened by a fixed collection of well-known nonlinear operators and an exhaustive search to find the best possible configuration for a given architecture, whose efficacy is further limited by a fixed output layer operator assignment. In this study, we leverage the Taylor series-based function approximation to propose a self-organizing variant of ONNs, Self-ONNs, for image restoration, which synthes

Author

Malik, Junaid; Kiranyaz, Serkan; Gabbouj, Moncef

Published

2020

Language

EN