Abstract
Image fusion is a process that integrates similar types of images collected from heterogeneous sources into one image in which the information is more definite and certain. Hence, the resultant image is anticipated as more explanatory and enlightening both for human and machine perception. Different image combination methods have been presented to consolidate significant data from a collection of images into one image. As a result of its applications and advantages in variety of fields such as remote sensing, surveillance, and medical imaging, it is significant to comprehend image fusion algorithms and have a comparative study on them. This paper presents a review of the present state-of-the-art and well-known image fusion techniques. The performance of each algorithm is assessed qualitatively and quantitatively on two benchmark multi-focus image datasets. We also produce a multi-focus image fusion dataset by collecting the widely used test images in different studies. The quantitative evaluation of fusion results is performed using a set of image fusion quality assessment metrics. The performance is also evaluated using different statistical measures. Another contribution of this paper is the proposal of a multi-focus image fusion library, to the best of our knowledge, no such library exists so far. The library provides implementation of numerous state-of-the-art image fusion algorithms and is made available publicly at project website.
Highlights
When camera types come into consideration, each type provides us images with different information such as the image captured by infrared camera offers information that lies in the infrared spectrum and digital color camera includes information that lies in the visible spectrum
In image fusion using discrete cosine transform based laplacian pyramid (DCTLP) algorithm [42], Discrete cosine transform (DCT) is used as a reduction function to form the Laplacian pyramid
We observed that the better performance of the spatial domain based methods is due to their accurate detection of focused and defocused regions which leads to crisp fusion results; such precise segmentation is not witnessed in most frequency domain methods that suffer with different artifacts e.g., ghost effect
Summary
When camera types come into consideration, each type provides us images with different information such as the image captured by infrared camera offers information that lies in the infrared spectrum and digital color camera includes information that lies in the visible spectrum Both of these sensors complement each other’s information, e.g., in surveillance applications, a better assessment can be done with image having both type of information. Image fusion makes interpretation better for the machine and human and reduces the image transmission cost [6,23,24] This reduction can be achieved by fusion, as after that there is no need to transmit multiple images of the same scene having the different part in focus.
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