Paper Info
Title
Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction.
Abstract
Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study.
Year
DOI
Venue
2017
10.1186/s13640-017-0203-4
EURASIP J. Image and Video Processing
Keywords
Field
DocType
Patch-based image denoising,Bilateral filter,Non-local means filtering,Probabilistic patch-based filtering,Dictionary learning filtering,K-SVD,Gaussian patch-PCA filtering,BM3D
Noise reduction,Computer vision,Median filter,Pattern recognition,Computer science,Non-local means,Image processing,Digital image,Step detection,Artificial intelligence,Digital image processing,Video denoising
Journal
Volume
Issue
ISSN
2017
1
1687-5176
Citations 
PageRank 
References 
5
0.43
30
Authors
2
Name
Order
Citations
PageRank
Monagi H. Alkinani163.19
Mahmoud R. El-Sakka28114.17