digital image processing
CHAPTER 1
INTRODUCTION
With the recent rapid developments in the field of sensing technologies multisensor systems have become a reality in a growing number of fields such as remote sensing, medical imaging, machine vision and the military applications for which they were first developed. The result of the use of these techniques is a great increase of the amount of data available. Image fusion provides an effective way of reducing this increasing volume of information while at the same time extracting all the useful information from the source images.
Multi-sensor data often presents complementary information about the region surveyed, so image fusion provides an effective method to enable comparison and analysis of such data.
The aim of image fusion, apart from reducing the amount of data, is to create new images that are more suitable for the purposes of human/machine perception, and for further image-processing tasks such as segmentation, object detection or target recognition in applications such as remote sensing and medical imaging. For example, visible-band and infrared images may be fused to aid pilots landing aircraft in poor visibility.
Multi-sensor images often have different geometric representations, which have to be transformed to a common representation for fusion. This representation should retain the best resolution of either sensor. A prerequisite for successful in image fusion is the alignment of multi-sensor images. Multi-sensor registration is also affected by the differences in the sensor images.
1
However, image fusion does not necessarily imply multi-sensor sources, there are interesting applications for both single-sensor and multi-sensor image fusion, as it will be shown in this report.
2
CHAPTER 2
IMAGE FUSION
2.1 DIGITAL IMAGE PROCESSING
Digital image processing is the use of computer algorithms to perform image processing on digital images.
As a subcategory or field of digital signal
INTRODUCTION
With the recent rapid developments in the field of sensing technologies multisensor systems have become a reality in a growing number of fields such as remote sensing, medical imaging, machine vision and the military applications for which they were first developed. The result of the use of these techniques is a great increase of the amount of data available. Image fusion provides an effective way of reducing this increasing volume of information while at the same time extracting all the useful information from the source images.
Multi-sensor data often presents complementary information about the region surveyed, so image fusion provides an effective method to enable comparison and analysis of such data.
The aim of image fusion, apart from reducing the amount of data, is to create new images that are more suitable for the purposes of human/machine perception, and for further image-processing tasks such as segmentation, object detection or target recognition in applications such as remote sensing and medical imaging. For example, visible-band and infrared images may be fused to aid pilots landing aircraft in poor visibility.
Multi-sensor images often have different geometric representations, which have to be transformed to a common representation for fusion. This representation should retain the best resolution of either sensor. A prerequisite for successful in image fusion is the alignment of multi-sensor images. Multi-sensor registration is also affected by the differences in the sensor images.
1
However, image fusion does not necessarily imply multi-sensor sources, there are interesting applications for both single-sensor and multi-sensor image fusion, as it will be shown in this report.
2
CHAPTER 2
IMAGE FUSION
2.1 DIGITAL IMAGE PROCESSING
Digital image processing is the use of computer algorithms to perform image processing on digital images.
As a subcategory or field of digital signal