Performance evaluation of image fusion methods

2017

As the size and cost of sensors decrease, sensor networks are increasingly becoming an attractive method to collect information in a given area. However, one single sensor is not capable of providing all the required information,either because of their design or because of observational constraints. One possible solution to get all the required information about a particular scene or subject is data fusion.. A small number of metrics proposed so far provide only a rough, numerical estimate of fusion performance with limited understanding of the relative merits of different fusion schemes. This paper proposes a method for comprehensive, objective, image fusion performance characterization using a fusion evaluation framework based on gradient information representation. We give the framework of the overall system and explain its usage method. The system has many functions: image denoising, image enhancement, image registration, image segmentation, image fusion, and fusion evaluation. ...

International Journal of Engineering Sciences & Research Technology, 2014

Image fusion techniques have interest within the remote sensing community. The reason of this is that in most cases the new generation of remote sensors with very high spatial resolution acquires image datasets in two separate modes: the highest spatial resolution is obtained for panchromatic images (PAN) whereas multispectral information (MS) is associated with lower spatial resolution. Usually, the term ‘fusion’ gets several words to appear, such as merging, combination, synergy, integration and several others that express more or less the same meaning the concept have since it appeared in literature. Image fusion techniques can be classified into three categories depending on the stage at which fusion takes place; it is often divided into three levels, namely: pixel level, feature level and decision level of representation. This paper describes the concept of image fusion and its relevant methods.

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.

Archives of Computational Methods in Engineering

Image fusion is the process in which substantial information taken through different sensors, different exposure values and at different focus points is integrated together to generate a composite image. In various applications, different types of data sets are captured with the help of different sensors like infrared (IR) region and visible region, Computed Tomography (CT) and Positron Emission Tomograph (PET) scan, multifocus images with different focal points and images taken by a static camera at different exposure values. A most promising area of image processing nowadays is image fusion. The picture fusion method seeks to incorporate two or more pictures into one picture that contains better data than each source picture without adding any artifacts. It plays an essential role in distinct applications like biomedical diagnostics, photography, object identification, surveillance, defense, and remote sensing satellite imaging. Three elements are taken into consideration in this review article that includes spatial domain fusion methodology, different transformation domain techniques, and image fusion performance evaluation metrics.

GRD Journals , 2019

Image fusion is process of combining two different images of same scene which are multi focused by nature. It has the major application in the field of visual sensor network for efficient surveillance monitoring. The thesis presents a novel hybrid image fusion technique that has the capabilities to be used in the real time environment such as central computer of visual sensor network for efficient surveillance purpose. A high resolution panchromatic image gives geometric details of an image because of the presence of natural as well as manmade objects in the scene and a low resolution multispectral image gives the color information of the source image. The aim of multisensor image fusion is to represent the visual information from multiple images having different geometric representations into a single resultant image without any information loss. The advantages of image fusion include image sharpening, feature enhancement, improved classification, and creation of stereo data sets. Multisensor image fusion provides the benefits in terms of range of operation, spatial and temporal characteristics, system performance, reduced ambiguity and improved reliability. Based on the processing levels, image fusion techniques can be divided into different categories. These are pixel level, feature level and symbol level/decision level. Pixel level method is the simplest and widely used method. This method processes pixels in the source image and retains most of the original image information. Compared to other two methods pixel level image fusion gives more accurate results. Feature level method processes the characteristics of the source image. This method can be used with the decision level method to fuse images effectively. Because of the reduced data size, it is easier to compress and transmit the data. The top level of image fusion is decision making level. It uses the data information extracted from the pixel level fusion or the feature level fusion to make optimal decision to achieve a specific objective. Moreover, it reduces the redundancy and uncertain information.

Transferring spatial details of a high-resolution image into a low-resolution one is called image fusion. There are some different fusion methods introduced. Due to the nature of fusion process, these methods may damage the spectral quality of the low-resolution multispectral image to a certain extent. In the literature, there are some metrics that are used to evaluate the quality of the fused images. Depending on their mathematical algorithms, these quality metrics may result in misleading results in terms of spectral quality in the fused images. If fusion process is successful, the classification result of the fused image should not be worse than the result acquired from raw multispectral image. In this study, Worldview-2, Landsat ETM+ and Ikonos multispectral images are fused with their own panchromatic bands and another Ikonos image is fused with Quickbird pan-sharpened image using IHS, CN, HPF, PCA, Multiplicative, Ehlers, Brovey, Wavelet, Gram-Schmidt and Criteria Based fusion methods. The fused images are then classified with Minimum Distance, Binary Encoding, Support Vector Machines, Random Forest, Maximum Likelihood and Artificial Neural Network classification methods by using exactly the same signatures. 450 points are used to calculate the post-classification accuracies of classified images. Some commonly-used metrics (Spectral and Spatial ERGAS, Spectral and Spatial RMSE, SID, SAM, RASE) are also calculated for all fused images. To determine the best fusion algorithm, the consistency between classification results and fusion metric results are evaluated together. HPF fusion method is found out to be the most successful fusion algorithm in terms of preserving the spectral quality and Support Vector Machines classifier is found out to be the best classification algorithm for these data sets.

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.

Scopus : Ilkogretim Online - Elementary Education Online, 2021; Vol 20 (Issue 3): pp. 4474-4485 http://ilkogretim-online.or, 2021

The goal of image fusion is to create an output picture that is more informative and valuable than any of the individual input images by combining information from all of the input photos. It raises the bar for how useful and accurate data may be. The quality of the resulting merged image changes with each use. Stereo camera fusion, medical imaging, monitoring production processes, electrical circuit design and inspection, sophisticated machine/device diagnostics, and intelligent robots on assembly lines are just few of the many applications of image fusion. Image filtering is one of the most fascinating uses of image processing. Size, shape, colour, depth, smoothness, etc. may all be tweaked with picture filtering. The basic idea is to use some kind of graphic design and editing software to manipulate the image's pixels until you get the result you want. This paper provides an overview of the many uses of image filtering methods.