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Gibbs Artifacts Removal with Nonlinearity.

Gengsheng L Zeng^1,2

¹Department of Computer Science, Utah Valley University, Orem, Utah, USA.

Journal of Biotechnology and Its Applications

|May 3, 2024

Summary

This summary is machine-generated.

This study demonstrates that simple convolutional neural networks (CNNs) can effectively remove Gibbs artifacts in image processing tasks like sharpening. Nonlinear functions and multiple channels are key to eliminating these image distortions.

Keywords:

Artifacts Image processing Index Terms Neural Network

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Area of Science:

Image Processing
Computer Vision
Machine Learning

Background:

Gibbs artifacts manifest as oscillations around sharp image edges, often occurring during deblurring or sharpening.
Linear methods struggle to mitigate these artifacts, highlighting the need for nonlinear approaches.

Purpose of the Study:

To investigate the efficacy of a simple convolutional neural network (CNN) in removing Gibbs artifacts during image sharpening.
To explore the role of nonlinear activation functions and network architecture in artifact reduction.

Main Methods:

A basic CNN with a single convolutional layer and four channels was employed.
The rectified linear unit (ReLU) served as the nonlinear activation function.

Main Results:

Gibbs artifacts were completely eliminated in simplified one-dimensional and two-dimensional test cases.
The underlying reasons for artifact removal by the CNN were elucidated.

Conclusions:

The study highlights the effectiveness of nonlinear functions and multi-channel processing in image artifact removal.
While CNNs show promise, the task can also be achieved through other non-neural network methods.