An Efficient Adaptive Denoising Algorithm for Remote Sensing Images
Typically, after the capturing, imaging, and transferring processes have been accomplished, the digital images will contain a variety of noise, caused by both the equipment itself and by the complex working environment. Consequently, it is necessary to perform a de-noising process to facilitate the...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2013-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2013/207461 |
id |
doaj-6561b4bebdae47cf83d8971073dd33ff |
---|---|
record_format |
Article |
spelling |
doaj-6561b4bebdae47cf83d8971073dd33ff2020-11-24T21:29:17ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/207461207461An Efficient Adaptive Denoising Algorithm for Remote Sensing ImagesXiujie Qu0Fu Zhang1Huan Jia2School of Information and Electronics, Beijing Institute of Technology, Microelectronics, Beijing 100081, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Microelectronics, Beijing 100081, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Microelectronics, Beijing 100081, ChinaTypically, after the capturing, imaging, and transferring processes have been accomplished, the digital images will contain a variety of noise, caused by both the equipment itself and by the complex working environment. Consequently, it is necessary to perform a de-noising process to facilitate the extraction of useful information. This paper presents a fast and efficient denoising algorithm, which combines the advantages of traditional median filters and weighted filter algorithms. In this algorithm, the noise in the figure is determined, and those results are applied to adaptively change the size of the window, while assigning different weights to the pixels in the filter window. The experimental results show that we can significantly remove almost all salt and pepper noise, while retaining full image textures, edges, and other minutiae.http://dx.doi.org/10.1155/2013/207461 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiujie Qu Fu Zhang Huan Jia |
spellingShingle |
Xiujie Qu Fu Zhang Huan Jia An Efficient Adaptive Denoising Algorithm for Remote Sensing Images Mathematical Problems in Engineering |
author_facet |
Xiujie Qu Fu Zhang Huan Jia |
author_sort |
Xiujie Qu |
title |
An Efficient Adaptive Denoising Algorithm for Remote Sensing Images |
title_short |
An Efficient Adaptive Denoising Algorithm for Remote Sensing Images |
title_full |
An Efficient Adaptive Denoising Algorithm for Remote Sensing Images |
title_fullStr |
An Efficient Adaptive Denoising Algorithm for Remote Sensing Images |
title_full_unstemmed |
An Efficient Adaptive Denoising Algorithm for Remote Sensing Images |
title_sort |
efficient adaptive denoising algorithm for remote sensing images |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2013-01-01 |
description |
Typically, after the capturing, imaging, and transferring processes have been accomplished, the digital images will contain a variety of noise, caused by both the equipment itself and by the complex working environment. Consequently, it is necessary to perform a de-noising process to facilitate the extraction of useful information. This paper presents a fast and efficient denoising algorithm, which combines the advantages of traditional median filters and weighted filter algorithms. In this algorithm, the noise in the figure is determined, and those results are applied to adaptively change the size of the window, while assigning different weights to the pixels in the filter window. The experimental results show that we can significantly remove almost all salt and pepper noise, while retaining full image textures, edges, and other minutiae. |
url |
http://dx.doi.org/10.1155/2013/207461 |
work_keys_str_mv |
AT xiujiequ anefficientadaptivedenoisingalgorithmforremotesensingimages AT fuzhang anefficientadaptivedenoisingalgorithmforremotesensingimages AT huanjia anefficientadaptivedenoisingalgorithmforremotesensingimages AT xiujiequ efficientadaptivedenoisingalgorithmforremotesensingimages AT fuzhang efficientadaptivedenoisingalgorithmforremotesensingimages AT huanjia efficientadaptivedenoisingalgorithmforremotesensingimages |
_version_ |
1725966377312845824 |