Filtering Techniques To Reduce Speckle Noise And Image Quality Enhancement Methods On Porous Silicon Images Layers
Recently, many studies have examined filters for reducing or removing speckle noise, which is inherent to different images types such as Porous Silicon (PS) images, in order to ameliorate the metrological evaluation of their applications. In the case of digital images, noise can produce difficulties...
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| Language: | English |
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OICC Press
2022-12-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/4970 |
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| author | Tifouti Issam Rahmouni Salah Meriane Brahim |
| author_facet | Tifouti Issam Rahmouni Salah Meriane Brahim |
| author_sort | Tifouti Issam |
| collection | DOAJ |
| description | Recently, many studies have examined filters for reducing or removing speckle noise, which is inherent to different images types such as Porous Silicon (PS) images, in order to ameliorate the metrological evaluation of their applications. In the case of digital images, noise can produce difficulties in the diagnosis of images details, such as edges and limits, should be preserved. Most algorithms can reduce or remove speckle noise, but they do not consider the conservation of these details. This paper describes in detail, the different techniques that focus mainly on the smoothing or elimination of speckle noise in images, as the aim of this study is to achieve the improvement of this smoothing and elimination, which is directly related to different processes (such as the detection of interest regions). Furthermore, the description of these techniques facilitates the operations of evaluations and research with a more specific scope. This study initially covers the definition and modeling of speckle noise. Then we elaborated in detail the different types of filters used in this study, finally, five statistical parameters such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Structural Similarity Index (SSIM), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR) are calculated, compared and the results are tabulated, common in filter evaluation processes. Trough the calculation of the statistical parameters, we can classify the filters in terms of perceptual quality by providing greater certainty. |
| format | Article |
| id | doaj-art-e8e4eebe26414df49a115b651966733e |
| institution | DOAJ |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-e8e4eebe26414df49a115b651966733e2025-08-20T03:03:54ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962022-12-0116410.30486/mjee.2022.696515Filtering Techniques To Reduce Speckle Noise And Image Quality Enhancement Methods On Porous Silicon Images LayersTifouti Issam0Rahmouni Salah1Meriane Brahim2Higher School for Professor Of Technological Education, (ENSET) Skikda, Algeria.Higher School for Professor Of Technological Education, (ENSET) Skikda, Algeria.Higher School for Professor Of Technological Education, (ENSET) Skikda, Algeria.Recently, many studies have examined filters for reducing or removing speckle noise, which is inherent to different images types such as Porous Silicon (PS) images, in order to ameliorate the metrological evaluation of their applications. In the case of digital images, noise can produce difficulties in the diagnosis of images details, such as edges and limits, should be preserved. Most algorithms can reduce or remove speckle noise, but they do not consider the conservation of these details. This paper describes in detail, the different techniques that focus mainly on the smoothing or elimination of speckle noise in images, as the aim of this study is to achieve the improvement of this smoothing and elimination, which is directly related to different processes (such as the detection of interest regions). Furthermore, the description of these techniques facilitates the operations of evaluations and research with a more specific scope. This study initially covers the definition and modeling of speckle noise. Then we elaborated in detail the different types of filters used in this study, finally, five statistical parameters such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Structural Similarity Index (SSIM), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR) are calculated, compared and the results are tabulated, common in filter evaluation processes. Trough the calculation of the statistical parameters, we can classify the filters in terms of perceptual quality by providing greater certainty.https://oiccpress.com/mjee/article/view/4970ANNGUIPorous SiliconPredictionPV panelSolar output |
| spellingShingle | Tifouti Issam Rahmouni Salah Meriane Brahim Filtering Techniques To Reduce Speckle Noise And Image Quality Enhancement Methods On Porous Silicon Images Layers Majlesi Journal of Electrical Engineering ANN GUI Porous Silicon Prediction PV panel Solar output |
| title | Filtering Techniques To Reduce Speckle Noise And Image Quality Enhancement Methods On Porous Silicon Images Layers |
| title_full | Filtering Techniques To Reduce Speckle Noise And Image Quality Enhancement Methods On Porous Silicon Images Layers |
| title_fullStr | Filtering Techniques To Reduce Speckle Noise And Image Quality Enhancement Methods On Porous Silicon Images Layers |
| title_full_unstemmed | Filtering Techniques To Reduce Speckle Noise And Image Quality Enhancement Methods On Porous Silicon Images Layers |
| title_short | Filtering Techniques To Reduce Speckle Noise And Image Quality Enhancement Methods On Porous Silicon Images Layers |
| title_sort | filtering techniques to reduce speckle noise and image quality enhancement methods on porous silicon images layers |
| topic | ANN GUI Porous Silicon Prediction PV panel Solar output |
| url | https://oiccpress.com/mjee/article/view/4970 |
| work_keys_str_mv | AT tifoutiissam filteringtechniquestoreducespecklenoiseandimagequalityenhancementmethodsonporoussiliconimageslayers AT rahmounisalah filteringtechniquestoreducespecklenoiseandimagequalityenhancementmethodsonporoussiliconimageslayers AT merianebrahim filteringtechniquestoreducespecklenoiseandimagequalityenhancementmethodsonporoussiliconimageslayers |