A Review of Image Fusion Techniques
---
paper_title: Analysis of Image Fusion Techniques based on Quality Assessment Metrics

paper_content:

Objective: The objective of Image Fusion is to combine the relevant and essential information from several images into a single image, which is highly informative than any of the source images such that the resultant fused image will be more appropriate for human visual perception and for image processing tasks like segmentation, feature extraction and object recognition. Methods: This paper presents the basic concepts, various types and levels of fusion, literature review of non-transform and transform based image fusion techniques from the perspective of their applications, advantages and limitations. Findings: The performance of existing image fusion methods along with various assessment metrics that determine the quality of fused images are evaluated and theoretically analyzed. It is found that the computational complexity is considerably reduced in Discrete Cosine Transformation based methods. Applications: Image Fusion has been effectively applied to many fields such as Remote Sensing, Military affairs, Machine Vision, Medical imaging, and so on.
---
paper_title: Pixel-level image fusion: A survey of the state of the art

paper_content:

This review provides a survey of various pixel-level image fusion methods according to the adopted transform strategy.The existing fusion performance evaluation methods and the unresolved problems are concluded.The major challenges met in different image fusion applications are analyzed and concluded. Pixel-level image fusion is designed to combine multiple input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. Due to this advantage, pixel-level image fusion has shown notable achievements in remote sensing, medical imaging, and night vision applications. In this paper, we first provide a comprehensive survey of the state of the art pixel-level image fusion methods. Then, the existing fusion quality measures are summarized. Next, four major applications, i.e., remote sensing, medical diagnosis, surveillance, photography, and challenges in pixel-level image fusion applications are analyzed. At last, this review concludes that although various image fusion methods have been proposed, there still exist several future directions in different image fusion applications. Therefore, the researches in the image fusion field are still expected to significantly grow in the coming years.
---
paper_title: Analysis of Image Fusion Techniques based on Quality Assessment Metrics

paper_content:

Objective: The objective of Image Fusion is to combine the relevant and essential information from several images into a single image, which is highly informative than any of the source images such that the resultant fused image will be more appropriate for human visual perception and for image processing tasks like segmentation, feature extraction and object recognition. Methods: This paper presents the basic concepts, various types and levels of fusion, literature review of non-transform and transform based image fusion techniques from the perspective of their applications, advantages and limitations. Findings: The performance of existing image fusion methods along with various assessment metrics that determine the quality of fused images are evaluated and theoretically analyzed. It is found that the computational complexity is considerably reduced in Discrete Cosine Transformation based methods. Applications: Image Fusion has been effectively applied to many fields such as Remote Sensing, Military affairs, Machine Vision, Medical imaging, and so on.
---
paper_title: Multifocus image fusion via the Hartley transform

paper_content:

The main flaw of optical lenses, especially for long focal lengths, is their limitation in depth of field (DOF). In other words, it is not possible to obtain an image with all subjects in focus: subjects that are in the DOF are focused, whereas other subjects are blurred. To obtain a result image with all objects in focus, several multifocus image fusion methods have been proposed. In this paper, the discrete Hartley transform (DHT) is used for this purpose. The main advantages of DHT are that it transforms real inputs into real outputs (as opposed to complex outputs as in the Discrete Fourier transform) and of being its own inverse. Our results demonstrate that the image obtained using the proposed method is multi-focused, with sharp edges for the different objects forming the image.
---
paper_title: Pixel-level image fusion: A survey of the state of the art

paper_content:

This review provides a survey of various pixel-level image fusion methods according to the adopted transform strategy.The existing fusion performance evaluation methods and the unresolved problems are concluded.The major challenges met in different image fusion applications are analyzed and concluded. Pixel-level image fusion is designed to combine multiple input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. Due to this advantage, pixel-level image fusion has shown notable achievements in remote sensing, medical imaging, and night vision applications. In this paper, we first provide a comprehensive survey of the state of the art pixel-level image fusion methods. Then, the existing fusion quality measures are summarized. Next, four major applications, i.e., remote sensing, medical diagnosis, surveillance, photography, and challenges in pixel-level image fusion applications are analyzed. At last, this review concludes that although various image fusion methods have been proposed, there still exist several future directions in different image fusion applications. Therefore, the researches in the image fusion field are still expected to significantly grow in the coming years.
---
paper_title: Pixel-level image fusion: A survey of the state of the art

paper_content:

This review provides a survey of various pixel-level image fusion methods according to the adopted transform strategy.The existing fusion performance evaluation methods and the unresolved problems are concluded.The major challenges met in different image fusion applications are analyzed and concluded. Pixel-level image fusion is designed to combine multiple input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. Due to this advantage, pixel-level image fusion has shown notable achievements in remote sensing, medical imaging, and night vision applications. In this paper, we first provide a comprehensive survey of the state of the art pixel-level image fusion methods. Then, the existing fusion quality measures are summarized. Next, four major applications, i.e., remote sensing, medical diagnosis, surveillance, photography, and challenges in pixel-level image fusion applications are analyzed. At last, this review concludes that although various image fusion methods have been proposed, there still exist several future directions in different image fusion applications. Therefore, the researches in the image fusion field are still expected to significantly grow in the coming years.
---
paper_title: Pixel-level image fusion: A survey of the state of the art

paper_content:

This review provides a survey of various pixel-level image fusion methods according to the adopted transform strategy.The existing fusion performance evaluation methods and the unresolved problems are concluded.The major challenges met in different image fusion applications are analyzed and concluded. Pixel-level image fusion is designed to combine multiple input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. Due to this advantage, pixel-level image fusion has shown notable achievements in remote sensing, medical imaging, and night vision applications. In this paper, we first provide a comprehensive survey of the state of the art pixel-level image fusion methods. Then, the existing fusion quality measures are summarized. Next, four major applications, i.e., remote sensing, medical diagnosis, surveillance, photography, and challenges in pixel-level image fusion applications are analyzed. At last, this review concludes that although various image fusion methods have been proposed, there still exist several future directions in different image fusion applications. Therefore, the researches in the image fusion field are still expected to significantly grow in the coming years.
---
paper_title: A new quality metric for image fusion

paper_content:

We present three variants of a new quality metric for image fusion. The interest of our metrics, which are based on an image quality index recently introduced by Wang and Bovik in [Z. Wang et al., March 2002], lies in the fact that they do not require a ground-truth or reference image. We perform several simulations which show that our metrics are compliant with subjective evaluations and can therefore be used to compare different image fusion methods or to find the best parameters for a given fusion algorithm.
---
paper_title: Analysis of Image Fusion Techniques based on Quality Assessment Metrics

paper_content:

Objective: The objective of Image Fusion is to combine the relevant and essential information from several images into a single image, which is highly informative than any of the source images such that the resultant fused image will be more appropriate for human visual perception and for image processing tasks like segmentation, feature extraction and object recognition. Methods: This paper presents the basic concepts, various types and levels of fusion, literature review of non-transform and transform based image fusion techniques from the perspective of their applications, advantages and limitations. Findings: The performance of existing image fusion methods along with various assessment metrics that determine the quality of fused images are evaluated and theoretically analyzed. It is found that the computational complexity is considerably reduced in Discrete Cosine Transformation based methods. Applications: Image Fusion has been effectively applied to many fields such as Remote Sensing, Military affairs, Machine Vision, Medical imaging, and so on.
---
paper_title: Image fusion using hierarchical PCA.

paper_content:

In this paper we propose image fusion algorithm using hierarchical PCA. Image fusion is a process of combining two or more images (which are registered) of the same scene to get the more informative image. Hierarchical multiscale and multiresolution image processing techniques, pyramid decomposition are the basis for the majority of image fusion algorithms. Principal component analysis (PCA) is a well-known scheme for feature extraction and dimension reduction and is used for image fusion. We propose image fusion algorithm by combining pyramid and PCA techniques and carryout the quality analysis of proposed fusion algorithm without reference image. There is an increasing need for the quality analysis of the fusion algorithms as fusion algorithms are data set dependent. Subjective analysis of fusion algorithm using hierarchical PCA is done by considering the opinion of experts and non experts and for quantitative quality analysis we use different quality metrics. We demonstrate fusion using pyramid, wavelet and PCA fusion techniques and carry out performance analysis for these four fusion methods using different quality measures for variety of data sets and show that proposed image fusion using hierarchical PCA is better for the fusion of multimodal imaged. Visible inspection with quality parameters are used to arrive at a fusion results.
---
paper_title: Pixel based and Wavelet based Image fusion Methods with their Comparative Study

paper_content:

Due to imperfections of imaging devices (optical degradations, limited resolution of CCD sensors) and instability of the observed scene (object motion, media turbulence), acquired images are often blurred, noisy and may exhibit insufficient spatial and/or temporal resolution. So, multi-sensor Image fusion is the process by which two or more images are combined into a single image retaining the important features from each of the original images. Important applications of the fusion of images include medical imaging, microscopic imaging, remote sensing, computer vision, and robotics. Fusion techniques include the simplest method of pixel averaging to more complicated methods such as principal component analysis and wavelet transform fusion which based on multiresolution. Several approaches to image fusion can be distinguished, depending on whether the images are fused in the spatial domain or they are transformed into another domain, and their transforms fused.
---
paper_title: Image Fusion of Digital Images

paper_content:

Image fusion is the process of combining information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or computer processing. The goal of image fusion is to integrate complementary multisensor, multitemporal and/or multiview information into one new image. The objective is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information particular to an application or task. This paper presents pixel based and region based fusion schemes, including a wavelet based algorithm. An area- based maximum selection rule, and consistency verification steps are used for feature selection. The algorithms are checked for multi sensor as well as multi focus image fusion. The performance evaluation was done based on various objective metrics and the results obtained were promising, comparable with existing algorithms. Index Terms—Image Fusion, multi sensor, multi focus, wavelet
---
paper_title: Analysis of Image Fusion Techniques based on Quality Assessment Metrics

paper_content:

Objective: The objective of Image Fusion is to combine the relevant and essential information from several images into a single image, which is highly informative than any of the source images such that the resultant fused image will be more appropriate for human visual perception and for image processing tasks like segmentation, feature extraction and object recognition. Methods: This paper presents the basic concepts, various types and levels of fusion, literature review of non-transform and transform based image fusion techniques from the perspective of their applications, advantages and limitations. Findings: The performance of existing image fusion methods along with various assessment metrics that determine the quality of fused images are evaluated and theoretically analyzed. It is found that the computational complexity is considerably reduced in Discrete Cosine Transformation based methods. Applications: Image Fusion has been effectively applied to many fields such as Remote Sensing, Military affairs, Machine Vision, Medical imaging, and so on.
---
paper_title: Multifocus image fusion in wavelet domain

paper_content:

How to select wavelet filters, decomposition levels and fusion schemes is of great concern fur multifocus image fusion in wavelet domain. We presented a simple image fusion algorithm based on wavelet transform. By wavelet transform, an image can be represented by a low frequency approximation, which contain the average information of the image, and several high frequency details with different scales and directions, which contain the texture or edge feature of the image. For the multifocus images, there are some areas unclear in certain source images which correspond to small wavelet coefficients, and clear in other source images which correspond to large coefficients. So we simply took the coefficients with greater modulus as the final coefficients to get the fusion image, which contains more details and is clearer in visual and more convenient for image analysis and understanding. We also discussed the influence of the wavelet filters, decomposition levels and fusion schemes on the fusion results, compared wavelet based fusion with other multiresolution fusions, such as Laplacian pyramid, gradient pyramid, contrast pyramid and ratio pyramid. Some results are obtained, which are of great value for research and experiment in this field.
---
paper_title: Medical image fusion based on Multi-Scaling (DRT) and Multi-Resolution (DWT) technique

paper_content:

Medical image fusion is the process of deriving important information from medical images like CT(Computed Tomography), MRI(Magnetic Resonance Imaging), PET(Positron Emission Tomography) and SPECT (Signal photon emission computed tomography). These derived information can be used for diagnosing diseases, detecting the tumor, surgery treatment and so on. The main objective of image fusion is to combine more useful information and remove redundant information from source images. This paper propose a hybrid image fusion method using the combine advantage of Multi-Scaling (DWT) and Multi-Resolution(DRT) Techniques on Medical images CT and MRI. The performance of the fused image is evaluated using different parameters like PSNR, MSE.
---
paper_title: Remote sensing image fusion using the curvelet transform

paper_content:

This paper presents an image fusion method suitable for pan-sharpening of multispectral (MS) bands, based on nonseparable multiresolution analysis (MRA). The low-resolution MS bands are resampled to the fine scale of the panchromatic (Pan) image and sharpened by injecting highpass directional details extracted from the high-resolution Pan image by means of the curvelet transform (CT). CT is a nonseparable MRA, whose basis functions are directional edges with progressively increasing resolution. The advantage of CT with respect to conventional separable MRA, either decimated or not, is twofold. Firstly, directional detail coefficients matching image edges may be preliminarily soft-thresholded to achieve a noise reduction that is better than that obtained in the separable wavelet domain. Secondly, modeling of the relationships between high-resolution detail coefficients of the MS bands and of the Pan image is more fitting, being accomplished in the directional multiresolution domain. Experiments are carried out on very-high-resolution MS+Pan images acquired by the QuickBird and Ikonos satellite systems. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed curvelet-based fusion method performs slightly better than the state-of-the art. Fusion tests at the full scale reveal that an accurate and reliable Pan-sharpening, little affected by local inaccuracies even in the presence of complex and detailed urban landscapes, is achieved by the proposed method.
---
paper_title: Analysis of Image Fusion Techniques based on Quality Assessment Metrics

paper_content:

Objective: The objective of Image Fusion is to combine the relevant and essential information from several images into a single image, which is highly informative than any of the source images such that the resultant fused image will be more appropriate for human visual perception and for image processing tasks like segmentation, feature extraction and object recognition. Methods: This paper presents the basic concepts, various types and levels of fusion, literature review of non-transform and transform based image fusion techniques from the perspective of their applications, advantages and limitations. Findings: The performance of existing image fusion methods along with various assessment metrics that determine the quality of fused images are evaluated and theoretically analyzed. It is found that the computational complexity is considerably reduced in Discrete Cosine Transformation based methods. Applications: Image Fusion has been effectively applied to many fields such as Remote Sensing, Military affairs, Machine Vision, Medical imaging, and so on.
---
paper_title: Analysis of Image Fusion Techniques based on Quality Assessment Metrics

paper_content:

Objective: The objective of Image Fusion is to combine the relevant and essential information from several images into a single image, which is highly informative than any of the source images such that the resultant fused image will be more appropriate for human visual perception and for image processing tasks like segmentation, feature extraction and object recognition. Methods: This paper presents the basic concepts, various types and levels of fusion, literature review of non-transform and transform based image fusion techniques from the perspective of their applications, advantages and limitations. Findings: The performance of existing image fusion methods along with various assessment metrics that determine the quality of fused images are evaluated and theoretically analyzed. It is found that the computational complexity is considerably reduced in Discrete Cosine Transformation based methods. Applications: Image Fusion has been effectively applied to many fields such as Remote Sensing, Military affairs, Machine Vision, Medical imaging, and so on.
---
paper_title: Analysis of Image Fusion Techniques based on Quality Assessment Metrics

paper_content:

Objective: The objective of Image Fusion is to combine the relevant and essential information from several images into a single image, which is highly informative than any of the source images such that the resultant fused image will be more appropriate for human visual perception and for image processing tasks like segmentation, feature extraction and object recognition. Methods: This paper presents the basic concepts, various types and levels of fusion, literature review of non-transform and transform based image fusion techniques from the perspective of their applications, advantages and limitations. Findings: The performance of existing image fusion methods along with various assessment metrics that determine the quality of fused images are evaluated and theoretically analyzed. It is found that the computational complexity is considerably reduced in Discrete Cosine Transformation based methods. Applications: Image Fusion has been effectively applied to many fields such as Remote Sensing, Military affairs, Machine Vision, Medical imaging, and so on.
---
paper_title: Analysis of Image Fusion Techniques based on Quality Assessment Metrics

paper_content:

Objective: The objective of Image Fusion is to combine the relevant and essential information from several images into a single image, which is highly informative than any of the source images such that the resultant fused image will be more appropriate for human visual perception and for image processing tasks like segmentation, feature extraction and object recognition. Methods: This paper presents the basic concepts, various types and levels of fusion, literature review of non-transform and transform based image fusion techniques from the perspective of their applications, advantages and limitations. Findings: The performance of existing image fusion methods along with various assessment metrics that determine the quality of fused images are evaluated and theoretically analyzed. It is found that the computational complexity is considerably reduced in Discrete Cosine Transformation based methods. Applications: Image Fusion has been effectively applied to many fields such as Remote Sensing, Military affairs, Machine Vision, Medical imaging, and so on.
---
paper_title: Analysis of Image Fusion Techniques based on Quality Assessment Metrics

paper_content:

Objective: The objective of Image Fusion is to combine the relevant and essential information from several images into a single image, which is highly informative than any of the source images such that the resultant fused image will be more appropriate for human visual perception and for image processing tasks like segmentation, feature extraction and object recognition. Methods: This paper presents the basic concepts, various types and levels of fusion, literature review of non-transform and transform based image fusion techniques from the perspective of their applications, advantages and limitations. Findings: The performance of existing image fusion methods along with various assessment metrics that determine the quality of fused images are evaluated and theoretically analyzed. It is found that the computational complexity is considerably reduced in Discrete Cosine Transformation based methods. Applications: Image Fusion has been effectively applied to many fields such as Remote Sensing, Military affairs, Machine Vision, Medical imaging, and so on.
---
paper_title: Analysis of Image Fusion Techniques based on Quality Assessment Metrics

paper_content:

Objective: The objective of Image Fusion is to combine the relevant and essential information from several images into a single image, which is highly informative than any of the source images such that the resultant fused image will be more appropriate for human visual perception and for image processing tasks like segmentation, feature extraction and object recognition. Methods: This paper presents the basic concepts, various types and levels of fusion, literature review of non-transform and transform based image fusion techniques from the perspective of their applications, advantages and limitations. Findings: The performance of existing image fusion methods along with various assessment metrics that determine the quality of fused images are evaluated and theoretically analyzed. It is found that the computational complexity is considerably reduced in Discrete Cosine Transformation based methods. Applications: Image Fusion has been effectively applied to many fields such as Remote Sensing, Military affairs, Machine Vision, Medical imaging, and so on.
---

Title: A Review of Image Fusion Techniques

Section 1: INTRODUCTION
Description 1: This section introduces the topic of image fusion, its importance, types, and the need for enhanced image fusion systems. It reviews the basics of image fusion, including spatial domain and transform domain techniques.

Section 2: LEVELS OF IMAGE FUSION
Description 2: This section explains the three fundamental levels of image fusion: Pixel-Level Fusion, Feature-Level Fusion, and Decision-Level Fusion, describing the methods and applications for each level.

Section 3: IMAGE FUSION METHODOLOGY
Description 3: This section outlines the generic steps involved in pixel-level image fusion, including the mathematical transformations, fusion techniques, and inverse transformations to generate the final fused image.

Section 4: EXAMPLES OF IMAGE FUSION
Description 4: This section provides examples of recent applications of image fusion in different domains such as remote sensing, medical diagnosis, and surveillance, illustrating the practical uses and benefits of image fusion techniques.

Section 5: Spatial Domain Techniques / Non-Transform Domain Techniques 
Description 5: This section discusses various existing spatial domain image fusion methods like Averaging, Brovey Method, Principal Component Analysis (PCA), and Intensity-Hue-Saturation (IHS), along with their advantages and limitations.

Section 6: Transform Domain Techniques / Frequency Domain Techniques
Description 6: This section elaborates on transform domain image fusion techniques such as Discrete Wavelet Transform (DWT), Curvelet Transform, Contourlet Transform, and Discrete Ripplet Transform, explaining their methodologies and applications.

Section 7: COMPARATIVE ANALYSIS
Description 7: This section provides a comparative analysis of various image fusion techniques in both non-transform and transform domains, illustrated with tables describing their performance and suitability for different applications.

Section 8: METRICS TO MEASURE FUSION PERFORMANCE
Description 8: This section describes the metrics and objective evaluation methods used to assess the quality of the fused images, including RMSE, PSNR, SSIM, Piella, Mean Gradient, and Spatial Frequency among others.

Section 9: CONCLUSION
Description 9: This section concludes the review by summarizing the advancements in image fusion and discussing the ongoing challenges and future directions for research in this domain.