| Autor: |
Hazique Aetesam, Suman Kumar Maji, Jerome Boulanger |
| Rok vydání: |
2022 |
| Zdroj: |
Advances in Computational Intelligence and Robotics ISBN: 9781799888925 |
| DOI: |
10.4018/978-1-7998-8892-5.ch020 |
| Popis: |
Remote sensing technologies such as hyperspectral imaging (HSI) and medical imaging techniques such as magnetic resonance imaging (MRI) form the pillars of human advancement. However, external factors like noise pose limitations on the accurate functioning of these imaging systems. Image enhancement techniques like denoising therefore form a crucial part in the proper functioning of these technologies. Noise in HSI and MRI are primarily a mixture of Gaussian and impulse noise. Image denoising techniques designed to handle mixed Gaussian-impulse (G-I) noise are thus an area of core research under the field of image restoration and enhancement. Therefore, this chapter discusses the mathematical preliminaries of G-I noise followed by an elaborate literature survey that covers the evolution of image denoising techniques for G-I noise from filtering-based to learning-based. An experimental analysis section is also provided that illustrates the performance of several denoising approaches under HSI and MRI, followed by a conclusion. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
