De-noising of microwave reflectometry signal using maximal overlap discrete wavelet packet transform for plasma density measurement

N., Subramaniyan; Buch, J.J.U.; Prince, A. Amalin; Pathak, Surya

doi:10.1016/j.measurement.2023.113564

Cited by 8 publications

(1 citation statement)

References 21 publications

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“…Furthermore, wavelet-based denoising methods have demonstrated their effectiveness in various applications, including medical imaging, satellite image processing, and digital photography. These techniques have played a crucial role in enhancing image quality for accurate analysis and interpretation, making them indispensable in many fields [65].…”

Section: Discussionmentioning

confidence: 99%

Wavelet Analysis for Image Denoising: A Multiscale Approach for Enhancing Visual Clarity

2023

Preprint

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This paper explores the application of wavelet analysis, a multiscale approach for enhancing visual clarity, in the context of image denoising, which reduces the impact of noisy pixels, caused by various factors such as electronic sensor limitations, low-light conditions, or transmission errors in digital imaging systems. This paper introduces some common noise types like Gaussian noise and processes of image denoising, eliciting the strength of wavelet analysis. As a powerful image denoising technology, wavelet analysis needs five steps to process the image, and the key step is thresholding. There are many kinds of wavelet, and each wavelet has different advantages and functions, which makes it suitable for different applications. Hence, wavelet analysis also makes a contribution in various fields beyond signal processing and data analysis, such as Biomedical Imaging and Geophysics and Seismology, where preserving image quality is essential for accurate analysis and interpretation. In short, this research highlights the promise of wavelet analysis, emphasizing the use of high- quality image data.

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Section: Discussionmentioning

confidence: 99%