Comparative study of Image Fusion Methods

InTech Education and …, 2011

Lecture Notes in Electrical Engineering, 2020

Image fusion is a technique of fusing multiple images for better information and a more accurate image compared to source images. The applications of image fusion in the modern military, multi-focus image integration, pattern recognition, remote sensing, biomedical imaging, etc. In this paper discussed, advantages and drawbacks of newly arrived existing methods in the transform domain and spatial domain image fusion, universal acceptable flowchart for image fusion obtained from literature, different helpful datasets that are accessible to assess extensively image fusion algorithms, many performance metrics used to measure the performance of image fusion techniques and finally suggestions are made as per present research necessities of respectable performance with acceptable effort in image fusion.

International Journal of Engineering & Technology

Fusion refers to combining two or more distinct things, the main objective of employing fusion is to generate results that provides the most detailed, reliable and accurate information possible. The image fusion is one of the main branchof data fusion. In image fusion the images are fused at different levels of images like pixel, feature and decision level. The necessity of image fusion for high resolution on multispectral and panchromatic images or realtime images for better vision. This paper reviews the general requirements of image fusion, widely used image fusion techniques such as PCA,IHS,DWT,NSCT etc.,summarizes the Quality Assessment Metrics in terms of metric, description and its principle, finally image fusion applications in various fields such as object detection, object identification, optimization and pattern reorganization, medical imaging, etc.,

2014

Pixel-level image fusion (PLIF) performance assessment includes information theory, feature-based, structural similarity, and perception-based objective metrics. However, to relate these metrics to human understanding requires subjective metrics. This paper proposes to use statistical analyses to assess PLIF performance over objective and subjective metrics. Nonparametric tests are applied to the subjective and objective assessment data from three multi-resolution image fusion methods using visual and infrared images. The tests can offer the performance information about the fusion algorithm at a designated significance level. Statistical analysis of PLIF facilitates the establishment of a baseline for the research in image fusion and serves as a statistical validation for proposing, comparing, and adopting a new PLIF algorithm.

Transactions on Engineering, …, 2005

In this paper, we present a novel objective nonreference performance assessment algorithm for image fusion. It takes into account local measurements to estimate how well the important information in the source images is represented by the fused image. The metric is based on the Universal Image Quality Index and uses the similarity between blocks of pixels in the input images and the fused image as the weighting factors for the metrics. Experimental results confirm that the values of the proposed metrics correlate well with the subjective quality of the fused images, giving a significant improvement over standard measures based on mean squared error and mutual information.

2005

Image fusion is and will be an integral part of many existing and future surveillance systems. However, little or no systematic attempt has been made up to now on studying the relative merits of various fusion techniques and their effectiveness on real multi-sensor imagery. In this paper we provide a method for evaluating the performance of image fusion algorithms. We define a set of measures of effectiveness for comparative performance analysis and then use them on the output of a number of fusion algorithms that have been applied to a set of real passive infrared (IR) and visible band imagery.

Defence Science Journal, 2008

With the availability of multi-sensor data in many fields such as remote sensing, medical imaging, machine vision and military applications, sensor image fusion has emerged as a new and promising field of research. The current definition of multisensor image fusion is very broad and the fusion can take place at signal, pixel, feature, and symbollevels. Image fusion provides the means to integrate complementary and redundant information from multiple images into a composite image more suitable for human visual perception, and computer processing such as segmentation, feature extraction, and target recognition. Integrating disparate information improves interpretation capabilities. This leads to more accurate analysis, increased utility, and more robust performance. Besides, the redundant information from images is encoded just once in the output. This results in a more efficient storage and dimensionality reduction in feature vectors. The main issue in fusion of many types of images for visual display is content preservation. Important details from all the input images should be preserved in the output image, while ensuring

2007

This paper deals with the problem of objective evaluation of dynamic, multi-sensor image fusion. For this purpose an established static image fusion evaluation framework, based on gradient information preservation between the inputs and the fused image, is extended to deal with additional scene and object motion information present in multi-sensor sequences. In particular formulations for dynamic, multi-sensor information preservation models are proposed to provide space-time localised fusion performance estimates. Perceptual importance distribution models are derived to accommodate temporal data and provide a natural generalisation of localised performance estimates into both global and continuous dynamic fusion performance scores. The proposed system is described in detail and shown to exhibit better evaluation accuracy, robustness and sensitivity when compared to existing dynamic fusion metrics on an evaluation of several established image fusion algorithms applied to multisensor sequences from an array of dynamic fusion scenarios.

Information Fusion, 2006

Image fusion algorithms attempt to produce a single fused image that is more informative than any of the multiple source images used to produce the fused image. Analytical studies of image fusion performance have been lacking. Such studies can augment existing experimental studies by addressing some aspects that are difficult to study using experimental methods. Here, an estimation theory approach is employed using a mathematical model based on the observation that each different sensor can provide a different quality when viewing a given object in the scene. One sensor may be better for viewing one object and a different sensor may be better for viewing a different object. The model also acknowledges that distortion and noise will enter into the sensor observations. This model allows us to employ known estimation theory techniques to find the best possible fusion performance, measured in terms of the standard estimation theory measure of performance. This performance measure has not yet received attention in the image fusion community. Some interesting results include the demonstration that a particular weighted averaging approach is shown to yield optimum estimation performance for the model we focus on. It is also shown that it is important to employ a priori information that describes which sensor is able to provide a good view of the important objects in the scene. The essential aspects of some frequently employed fusion approaches are studied and the capabilities of these approaches are analyzed and compared to the best fusion algorithms. We hope this study will encourage further analytical studies of image fusion.

International Journal of Computer Applications, 2010

Remote sensing is defined as obtaining information about a Performance metrics for measuring absolute degradation and their gain in fused image quality are proposed when fusing noisy input modalities. This considers fusion of noise patterns, is also developed and used to evaluate the perceptual effect of noise corrupting homogenous image regions (i.e. areas with no salient features). These metrics are employed to compare the performance of different image fusion methodologies and feature selection/information fusion strategies operating under noisy input conditions. The aim of this paper is to define appropriate metrics which measure the effects of input sensor noise on the performance of image fusion systems.' noisy fusion'' metrics are developed and used, in the first two scenarios, to measure the effects of additive sensor noise on the performance of several signal-level image fusion algorithms operating across a range of input signal-to-noise ratio (SNR) values.