Salt and pepper denoising filters for digital images: A technical review
Noise in images refers to random variations in pixel intensities that alter the original pixel intensities of the image. Among the various noises present in the image, salt and pepper noise corrupts images due to a defect in the device’s hardware or the camera’s faulty sensor. This leads to...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Technical Sciences in Cacak
2024-01-01
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| Series: | Serbian Journal of Electrical Engineering |
| Subjects: | |
| Online Access: | https://doiserbia.nb.rs/img/doi/1451-4869/2024/1451-48692403429K.pdf |
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| Summary: | Noise in images refers to random variations in pixel intensities that alter
the original pixel intensities of the image. Among the various noises
present in the image, salt and pepper noise corrupts images due to a defect
in the device’s hardware or the camera’s faulty sensor. This leads to
misinterpretation of pixels and deterioration of image quality during
visualization of natural images and diagnosis of medical images. Up until
now, researchers have presented several cutting-edge filters to overcome and
lessen the impact of this noise. This article presents a comprehensive
investigation into three different domains of impulse denoising of digital
images. These domains are based on the spatial domain, the fuzzy logic
domain, and the deep learning-based category. In this study, many techniques
of image denoising were categorized and analyzed, along with their
respective motivations, principles of execution, and comparative analysis.
We carefully explain and implement a few significant approaches, considered
state-of-the-art in each subject, in MATLAB. When doing simulations, the
filters are analyzed and quantitatively evaluated using three metrics that
are frequently utilized. These parameters are the peak signal-to-noise ratio
(PSNR) and the structural similarity index measure (SSIM). Finally, we
provide a comparison of each study category to enhance our comprehension of
each domain. We conclude by outlining the challenges each domain poses and
providing a detailed explanation of the rationale for future research. |
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| ISSN: | 1451-4869 2217-7183 |