Image Fusion Research Paper

CHAPTER1 INTRODUCTION

1.1What is an image? An image is an array, or a matrix, of square pixels (picture elements) arranged in columns and rows. Figure 1.1: An image — an array or a matrix of pixels arranged in columns and rows.

In a (8-bit) grayscale image each picture element has an assigned intensity that ranges from 0 to 255. A grey scale image is what people normally call a black and white image, but the name emphasizes that such an image will also include many shades of grey. Figure 1.2: Pixel representation

Each pixel has a value from 0 (black) to 255 (white).

The possible range of the pixel values depend on the colour depth of the image, here 8 bit = 256 tones or grayscales.

1.2 Colour image

A normal

The integration of data, recorded from a multi-sensor system, together with knowledge, is known as data fusion. The fused image should have more useful information content compared to the individual image. The different image fusion methods can be evaluated using different fusion parameters and each parameter varies due to different fusion rule effect. In general, the parameters used to design fusion rules is based on experiments or it adaptively changes with the change in image contents so it is very difficult to get the optimal fusion effect which can preserve all important information from the source images. Image fusion system has several advantages over single image source and resultant fused image should have higher signal to noise ratio, increased robustness and reliability in the event of sensor failure, extended parameter coverage and rendering a more complete picture of the system. The actual fusion process can take place at different levels of information representation. A common categorization is to distinguish between pixel, feature and decision level, although there may be crossings between them. Image fusion at pixel level amounts to integration of low-level information, in most cases physical measurements such as intensity. Generally, the pixel based image fusion methods average pixel intensity values of the

On the other hand image fusion reduces the amount of data and creates new images which are more suitable for human perception or further processing of images. Image fusion improves the reliability (by redundant information) and capability of the images (by redundant information). The image fusion can be used in target reorganization, object detection, military applications, medical imaging, Industrial applications, computer vision and robotics. Following figure shows the concept of image