Image Denoising using Adaptive Filter - Couverture souple

Jayaswal, Anupkumar; Dembrani, Mahesh; Seragi, Sattyendra

 
9783330082175: Image Denoising using Adaptive Filter
Couverture souple
ISBN 10 : 3330082178 ISBN 13 : 9783330082175
Editeur : LAP LAMBERT Academic Publishing, 2017

Synopsis

Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This thesis reviews the existing denoising algorithms, such as Mean filter, Median filter, Local adaptive filter and finely two dimensional blocks least mean square algorithm (TDBLMS) filtering approach, and performs their comparative study. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise and Poisson noise. Selection of the denoising algorithm is application dependent. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. The TDBLMS approach finds applications in denoising of different images corrupted with Additive and multiplicative noise.

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Présentation de l'éditeur

Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This thesis reviews the existing denoising algorithms, such as Mean filter, Median filter, Local adaptive filter and finely two dimensional blocks least mean square algorithm (TDBLMS) filtering approach, and performs their comparative study. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise and Poisson noise. Selection of the denoising algorithm is application dependent. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. The TDBLMS approach finds applications in denoising of different images corrupted with Additive and multiplicative noise.

Biographie de l'auteur

Prof. A.B Jayawal working as ans ASST. Prof. in RCPIT Shirpur since 2010. The area of interest is image processing and digital signal processing.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2017
Langue
anglais
ISBN 10 
3330082178
ISBN 13 
9783330082175
Reliure
Broché
Nombre de pages
76
Coordonnées du fabricant
non disponible
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