14-06-2012, 05:30 PM
Image Fusion Algorithm for Impulse Noise Reduction
Abstract
This paper introduces the concept of image fusion of
noisy images for impulse noise reduction. Image fusion is the
process of combining two or more images into a single image
while retaining the important features of each image. Multiple
image fusion is an important technique used in military, remote
sensing and medical applications.
INTRODUCTION
Digital images are often corrupted during acquisition,
transmission or due to faulty memory locations in hardware
[1]. The impulse noise can be caused by a camera due to the
faulty nature of the sensor or during transmission of coded
images in a noisy communication channel [2]. Consequently,
some pixel intensities are altered while others remain noise
free. The noise density (severity of the noise) varies depending
on various factors namely reflective surfaces, atmospheric
variations, noisy communication channels and so on.
IMPULSE NOISE IN IMAGES
Impulse noise corruption is very common in digital
images. Impulse noise is always independent and uncorrelated
to the image pixels and is randomly distributed over the
image. Hence unlike gaussian noise, for an impulse noise
corrupted image all the image pixels are not noisy, a number
of image pixels will be noisy and rest of the pixels will be
noise free. There are different types of impulse noise namely
salt and pepper type of noise and random valued impulse
noise.
THE PROPOSED TECHNIQUES
Two different techniques are proposed in this paper for
obtaining higher quality images by image fusion. In the first
technique the noisy images are combined by fusion and then
the fused image undergoes further noise detection and
filtering. The second technique is an image fusion of
individually denoised images using a novel fusion criterion.
The techniques are detailed below.
CONCLUSIONS
In this paper, we proposed fusion techniques for impulse
noise corrupted images. The proposed techniques help to attain
higher quality images. Images of an object or scene captured
by multiple sensors can be fused by the proposed techniques.
Technique I is equivalent of capturing a less noisy image than
what is achievable with the available sensors.
Abstract
This paper introduces the concept of image fusion of
noisy images for impulse noise reduction. Image fusion is the
process of combining two or more images into a single image
while retaining the important features of each image. Multiple
image fusion is an important technique used in military, remote
sensing and medical applications.
INTRODUCTION
Digital images are often corrupted during acquisition,
transmission or due to faulty memory locations in hardware
[1]. The impulse noise can be caused by a camera due to the
faulty nature of the sensor or during transmission of coded
images in a noisy communication channel [2]. Consequently,
some pixel intensities are altered while others remain noise
free. The noise density (severity of the noise) varies depending
on various factors namely reflective surfaces, atmospheric
variations, noisy communication channels and so on.
IMPULSE NOISE IN IMAGES
Impulse noise corruption is very common in digital
images. Impulse noise is always independent and uncorrelated
to the image pixels and is randomly distributed over the
image. Hence unlike gaussian noise, for an impulse noise
corrupted image all the image pixels are not noisy, a number
of image pixels will be noisy and rest of the pixels will be
noise free. There are different types of impulse noise namely
salt and pepper type of noise and random valued impulse
noise.
THE PROPOSED TECHNIQUES
Two different techniques are proposed in this paper for
obtaining higher quality images by image fusion. In the first
technique the noisy images are combined by fusion and then
the fused image undergoes further noise detection and
filtering. The second technique is an image fusion of
individually denoised images using a novel fusion criterion.
The techniques are detailed below.
CONCLUSIONS
In this paper, we proposed fusion techniques for impulse
noise corrupted images. The proposed techniques help to attain
higher quality images. Images of an object or scene captured
by multiple sensors can be fused by the proposed techniques.
Technique I is equivalent of capturing a less noisy image than
what is achievable with the available sensors.