Satellite image denoising via adjustment of the quaternionic wavelet coefficients

Kadiri, Mohammed; Zigh, Ehlem; Djebbouri, M.

doi:10.1109/icee-b.2017.8192127

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…This method seeks to optimize the wavelet with the assisted Harris Hawks optimization algorithm. In the work of Mohammed, K. et al [10] , a Thresholding Neural Network (TNN) was developed for adaptive noise reduction. New types of soft and hard thresholding functions are created to serve as the activation function of the TNN.…”

Section: Image Denoisingmentioning

confidence: 99%

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Wavelet Transform Technique Applied to Satellite Image Denoising

Goni,

Ahmadu,

Malgwi

2023

electr. sci. & eng.

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Satellite images either digital or analog must have certain elements that are accidentally introduced during the processing of capturing as a result of weather or system sensor known as electronic noise. However, several attempts and advances have been made by academicians, industries and intelligent security agencies to remove this noise. It has been a nagging problem in the area of computer vision, image processing and artificial intelligence to denoise satellite images and noise removal is among the significant components in satellite image analysis. The aim of this research work was to denoise the satellite image of Sambisa forest using the wavelet transform technique. Satellite images of Sambisa forest captured by Landsat satellite in 2007, 2013, 2014, 2019 and 2021 respectively with their associated Geo-referenced 11.2503° N Longitude and 13.4167° E Latitude were downloaded from the United States Geological Survey (USGS) website. The images are acquired as Zipped Geo-referenced Tagged Image File Format (GeoTIFF). Color Composite bands of natural colors (bands 2, 3 and 4) are combined using the ArcGIS software and RGB image were obtained. Wavelet transforms denoising technique was used to filter noise from the images, which was implemented using the wdenoise2() function in MATLAB 2021.

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Section: Image Denoisingmentioning

confidence: 99%

“…The results show the superiority of the developed method as compared with the previous TNN and optimization-based noise suppression methods. This improved function is shown in Equation (10).…”

Section: Introductionmentioning

confidence: 99%