Edge-Preserving Image Denoising Based on Lipschitz Estimation
The information transmitted in the form of signals or images is often corrupted with noise. These noise elements can occur due to the relative motion, noisy channels, error in measurements, and environmental conditions (rain, fog, change in illumination, etc.) and result in the degradation of images...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/11/5126 |
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| author | Bushra Jalil Zunera Jalil Eric Fauvet Olivier Laligant |
| author_facet | Bushra Jalil Zunera Jalil Eric Fauvet Olivier Laligant |
| author_sort | Bushra Jalil |
| collection | DOAJ |
| description | The information transmitted in the form of signals or images is often corrupted with noise. These noise elements can occur due to the relative motion, noisy channels, error in measurements, and environmental conditions (rain, fog, change in illumination, etc.) and result in the degradation of images acquired by a camera. In this paper, we address these issues, focusing mainly on the edges that correspond to the abrupt changes in the signal or images. Preserving these important structures, such as edges or transitions and textures, has significant theoretical importance. These image structures are important, more specifically, for visual perception. The most significant information about the structure of the image or type of the signal is often hidden inside these transitions. Therefore it is necessary to preserve them. This paper introduces a method to reduce noise and to preserve edges while performing Non-Destructive Testing (NDT). The method computes Lipschitz exponents of transitions to identify the level of discontinuity. Continuous wavelet transform-based multi-scale analysis highlights the modulus maxima of the respective transitions. Lipschitz values estimated from these maxima are used as a measure to preserve edges in the presence of noise. Experimental results show that the noisy data sample and smoothness-based heuristic approach in the spatial domain restored noise-free images while preserving edges. |
| first_indexed | 2024-03-10T10:50:14Z |
| format | Article |
| id | doaj.art-df47364b8ffb494eb2537384a05597d7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:50:14Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-df47364b8ffb494eb2537384a05597d72023-11-21T22:17:45ZengMDPI AGApplied Sciences2076-34172021-05-011111512610.3390/app11115126Edge-Preserving Image Denoising Based on Lipschitz EstimationBushra Jalil0Zunera Jalil1Eric Fauvet2Olivier Laligant3Istituto di Scienza e Tecnologie dell’Informazione “Alessandro Faedo” CNR, 56124 Pisa, ItalyDepartment of Cyber Security, Air University, Islamabad 44000, PakistanLe2i, VIBOT ERL CNRS 6000, Universite de Dijon, 71200 Dijon, FranceLe2i, VIBOT ERL CNRS 6000, Universite de Dijon, 71200 Dijon, FranceThe information transmitted in the form of signals or images is often corrupted with noise. These noise elements can occur due to the relative motion, noisy channels, error in measurements, and environmental conditions (rain, fog, change in illumination, etc.) and result in the degradation of images acquired by a camera. In this paper, we address these issues, focusing mainly on the edges that correspond to the abrupt changes in the signal or images. Preserving these important structures, such as edges or transitions and textures, has significant theoretical importance. These image structures are important, more specifically, for visual perception. The most significant information about the structure of the image or type of the signal is often hidden inside these transitions. Therefore it is necessary to preserve them. This paper introduces a method to reduce noise and to preserve edges while performing Non-Destructive Testing (NDT). The method computes Lipschitz exponents of transitions to identify the level of discontinuity. Continuous wavelet transform-based multi-scale analysis highlights the modulus maxima of the respective transitions. Lipschitz values estimated from these maxima are used as a measure to preserve edges in the presence of noise. Experimental results show that the noisy data sample and smoothness-based heuristic approach in the spatial domain restored noise-free images while preserving edges.https://www.mdpi.com/2076-3417/11/11/5126denoisingedge detectionLipschitz estimationcontinuous wavelet transform |
| spellingShingle | Bushra Jalil Zunera Jalil Eric Fauvet Olivier Laligant Edge-Preserving Image Denoising Based on Lipschitz Estimation Applied Sciences denoising edge detection Lipschitz estimation continuous wavelet transform |
| title | Edge-Preserving Image Denoising Based on Lipschitz Estimation |
| title_full | Edge-Preserving Image Denoising Based on Lipschitz Estimation |
| title_fullStr | Edge-Preserving Image Denoising Based on Lipschitz Estimation |
| title_full_unstemmed | Edge-Preserving Image Denoising Based on Lipschitz Estimation |
| title_short | Edge-Preserving Image Denoising Based on Lipschitz Estimation |
| title_sort | edge preserving image denoising based on lipschitz estimation |
| topic | denoising edge detection Lipschitz estimation continuous wavelet transform |
| url | https://www.mdpi.com/2076-3417/11/11/5126 |
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