Goal Of Image Fusion Research Articles

DSFusion: Infrared and visible image fusion method combining detail and scene information

The goal of image fusion is to combine the complementary features of two images to generate an information-rich fused image. However, taking into account both detail and scene information is difficult for existing image fusion algorithms. Therefore, we propose an infrared and visible image fusion method combining details and scene information (DSFusion). Specifically, we first design a local attention module (LAM), which performs feature extraction on the source image from multiple perspectives in order to better preserve minute detail information. Moreover, in order to distinguish and highlight the differences between the two modal images, we improved the channel attention module. Finally, we design a new loss function that can effectively balance the detail and scene information of the fusion image. Extensive testing on publicly available datasets demonstrates that DSFusion surpasses state of the art in both qualitative and quantitative evaluation. Furthermore, promising outcomes have been obtained in generalization experiments by directly expanding the trained model to other datasets, indicating the model’s excellent generalization capability. The code is available at https://github.com/LKZ1584905069/DSFusion.

Optimal fusion of multi-focus image: Integrating WNMF and focal point analysis

Image fusion can be used to improve the image utilization, spatial resolution and spectral resolution, which has been widely applied on medicine, remote sensing, computer vision, weather forecast and military target recognition. The goal of image fusion is to reduce the uncertainty and redundancy of the output and increase the reliability of the image on the basis of the maximum combination of relevant information. In this paper, a multi-focus image fusion algorithm based on WNMF and Focal point position analysis is proposed to improve the image fusion method based on nonnegative matrix factorization. In the imaging process, the Gaussian function is used to approximate the point spread function in the optical system. Then calculate the difference between the original image and the approximate point spread function and get the weighted matrix [Formula: see text]. Finally, we apply the weighted nonnegative matrix algorithm to image fusion, and the new fusion image with clear parts is obtained. Experimental results show that the multi-focus image fusion algorithm based on WNMF and Focal point position analysis (MFWF) is better.

Alpha Stable Filter and Distance for Multifocus Image Fusion

Image fusion, an important branch of data fusion, is the process of combining relevant information from two or more images into a single image where the resulting image will be more informative than any of the input images. The result image should be more suitable for visual perception and machine perception or computer processing. The goal of image fusion is to reduce uncertainty and minimize redundancy in the output as well maximize relevant information particular to an application or task. Mutlifocus image can be fused using Laplacian Pyramid (LP). LP is computed using two basic operation: reduce and expand that involve low-pas filter. The filter used in LP is Gaussian. In this paper, we propose a multifocus image fusion substituting in LP the Gaussian filter by Alpha Stable filters and using an adapted distance as integration rule in LP. We apply this method to multifocus images where the blurred part is generated using Gaussian filter and we compare with some news methods. The proposed method give the better results.

Multi-focusing algorithm for microscopy imagery in assembly line using low-cost camera

We propose an algorithm to perform multi-focus image fusion and integrate a set of images acquired at different focus settings to a single uniformly focused image for visual inspection in assembly lines. The goal of image fusion is to integrate complementary image multi-view information from standard, low resolution assembly line camera into one new image, the quality of which could not be achieved using other methods such as direct digital photography. Our method is based on the image decomposition into Gaussian pyramids, generation of the Laplacian pyramids, and image reconstruction from the Laplacian pyramids. The main characteristics of the proposed method include good quality of integrated multi-focus image, and suitability for microscopy conveyor applications given movement of objects, different lighting conditions, and positional misalignments. We have evaluated our method using eight image quality metrics yielding good results (best results were obtained using the Tenengrad (TENG) method, reaching an accuracy of 0.982) with a low-cost camera and computationally efficient implementation.

Statistical Measurements of Multi Modal MRI – PET Medical Image Fusion using 2D – HT in HSV color Space

Abstract The goal of image fusion is to obtain large amount of information into a single image with more quantitatively and qualitatively. The image fusion can be applicable in various fields like multi-focus, multi-modal medical, satellite etc. This paper proposed statistical measurements of Multimodal MRI-PET medical image fusion using 2D Hartley transform (HT) in HSV color space. This proposed method was discussed with two different modalities of medical images like MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) and also discussed with five steps. Initially the PET color image is converted into HSV channels. Second step is MRI and V component of PET image are divided into 8*8 blocks and then apply 2D Hartley transform on each block of two input images. Third step is compute variance of each block of two images and then select best blocks. Fourth step is applying inverse 2D HT and all blocks are arranged into single image i.e. new V component. Finally concordination of New V component, H, S to get HSV image and then convert HSV to RGB to obtain final fused image with more accurate. The result shows the importance of the proposed image and this is superior to existing methods like DFT along with Smooth, Hartley along with Smooth, Hartley along with Mean, DFT along with Mean and DCT along with Smooth. The evaluation parameters such as Mean, Standard Deviation and Gradient plays a major role in image fusion for testing the quality.

Multi-focus image fusion based on sparse decomposition and background detection

The goal of image fusion is to accurately and comprehensively describe complementary information of multiple source images in a new scene. Traditional fusion methods are easy to produce side-effects which cause artifacts and blurred edges. To solve these problems, a novel fusion algorithm based on robust principal component analysis (RPCA) and guided filter is proposed. The guided filter can preserve the edges effectively, which is often used to enhance the images without distort the details. Considering edges and flat area are treated differently by the guided filter, in this paper, sparse component of the source image is filtered by the guided filter to generate the enhanced image which contains the preserved edges and the enhanced background. And then the focused regions of the source images are detected by spatial frequency map of the difference images between the enhanced image and the corresponding source image. Finally, morphological algorithm is used to obtain precise fusion decision map. Experimental results show that the proposed method improves the fusion performance obviously which outperforms the current fusion methods.

Framework for Hyperspectral Image Segmentation using Unsupervised Algorithm

Hyperspectral imaging system contains stack of images collected from the sensor with different wavelengths representing the same scene on the earth. This paper presents a framework for hyperspectral image segmentation using a clustering algorithm. The framework consists of four stages in segmenting a hyperspectral data set. In the first stage, filtering is done to remove noise in image bands. Second stage consists of dimensionality reduction algorithms, in which the bands that convey less information or redundant data will be removed. This deletion will decrease the storage requirement, computational load etc in processing the hyperspectral data. In the third stage, the informative bands which are selected in the second stage are merged into a single image using hierarchical fusion technique. The main goal of image fusion is to combine all the information from the selected image bands to form a single image. This single image is segmented using Fuzzy C-means (FCM) algorithm. The qualitative and quantitative analysis shows that this framework will segment the data set more accurately by combining all the features in the image bands.

Image Fusion with Contrast Improving and Feature Preserving

The goal of image fusion is to obtain a fused image that contains most significant information in all input images which were captured by different sensors from the same scene. In particular, the fusion process should improve the contrast and keep the integrity of significant features from input images. In this paper, we propose a region-based image fusion method to fuse spatially registered visible and infrared images while improving the contrast and preserving the significant features of input images. At first, the proposed method decomposes input images into base layers and detail layers using a bilateral filter. Then the base layers of the input images are segmented into regions. Third, a region-based decision map is proposed to represent the importance of every region. The decision map is obtained by calculating the weights of regions according to the gray-level difference between each region and its neighboring regions in the base layers. At last, the detail layers and the base layers are separately fused by different fusion rules based on the same decision map to generate a final fused image. Experimental results qualitatively and quantitatively demonstrate that the proposed method can improve the contrast of fused images and preserve more features of input images than several previous image fusion methods.

Infrared Target Detection and Location for Visual Surveillance Using Fusion Scheme of Visible and Infrared Images

The main goal of image fusion is to combine substantial information from different images of the same scene into a single image that is suitable for human and machine perception or for further image-processing tasks. In this study, a simple and efficient image fusion approach based on the application of the histogram of infrared images is proposed. A fusion scheme to select adaptively weighted coefficients for preserving salient infrared targets from the infrared image and for obtaining most spatial detailed information from the visible image is presented. Moving and static infrared targets in the fused image are labeled with different colors. This technique enhances perception of the image for the human visual system. In view of the modalities of infrared images, low resolution, and low signal-to-noise ratio, an anisotropic diffusion equation model is adopted to remove noise and to effectively preserve edge information before the fusion stage. By using the proposed method, relevant spatial information is preserved and infrared targets are clearly identified in the resulting fused images.

Multiscale image fusion using an adaptive similarity-based sensor weighting scheme and human visual system-inspired contrast measure

The goal of image fusion is to combine multiple source images obtained using different capture techniques into a single image to provide an effective contextual enhancement of a scene for human or machine perception. In practice, considerable value can be gained in the fusion of images that are dissimilar or complementary in nature. However, in such cases, global weighting schemes may not sufficiently weigh the contribution of the pertinent information of the source images, while existing adaptive schemes calculate weights based on the relative amounts of salient features, which can cause severe artifacting or inadequate local luminance in the fusion result. Accordingly, a new multiscale image fusion algorithm is proposed. The approximation coefficient fusion rule of the algorithm is based on a novel similarity based weighting scheme capable of providing improved fusion results when the input source images are either similar or dissimilar to each other. Moreover, the algorithm employs a new detail coefficient fusion rule integrating a parametric multiscale contrast measure. The parametric nature of the contrast measure allows the degree to which psychophysical laws of human vision hold to be tuned based on image-dependent characteristics. Experimental results illustrate the superior performance of the proposed algorithm qualitatively and quantitatively.

A Novel Image Fusion Approach Based on Rough Set and Wavelet Analysis

The goal of image fusion is to combine a high-quality image from multi-image about the same object. The paper presents an image fusion scheme based on wavelet transform and rough set. Firstly, the two images are decomposed by orthogonal wavelet; the image’s wavelet coefficients are got. Comparing with the two image’s wavelet coefficients, wavelet coefficients’ matrix is composed of maximum absolute value, the fused image is obtained by the inverse wavelet transform. The last section of the paper verifies the method by experiment and gets the good experimental results.

Medical image registration

Problems related to file structures, transfer and networking plays an important role is in medical imaging in the early detection of cancers. Radiologists often have difficulty locating and accurately identifying cancer tissue, even with the aid of structural information such as CT and MRI because of the low contrast between the cancer and the surrounding tissues in CT and MRI images. Using SPECT and radioactively labeled monoclonal antibodies it is possible to obtain high contrast images of the concentration of antibodies in tumors. However, sometimes it is difficult to determine the precise location ofthe high concentration of the radioactive isotope in SPECT or PET images in relation to anatomic structures, such as vital organs and surrounding healthy tissue. Image registration is a visualization tool that can significantlyaid in the early detection oftumors and other diseases, and aid in improving the accuracy of diagnosis. The problems related to multimodality imaging can be separated into three groups; a) problems related to file structures, transfer and networking, b) registration, and c) visualization of the composite images. an image with normal uptake properties to an image with suspected abnormalities. In addition, image registration of SPECT and PET images and the registration of SPECT and PET images with anatomic atlases, provide an important means to evaluate comparative uptake properties of SPECT and PET radiopharmaceuticals, and to correlate uptake properties with anatomy. Medical image registration has been applied to the diagnosis of breast cancer, colon cancer, cardiac studies, wrist and other injuries, inflammatory diseases and different neurological disorders including brain tumors, Alzheimer's disease and schizophrenia. This method has also been utilized in radiotherapy, mostly for brain tumors, and by cranio-facial surgeons to prepare for and simulate complexsurgical procedures. One area where image registration Two basic types of medical images are made: functional body images (such as SPECT or PET scans), which provide physiological information, and structural images (such as CT or MRI), which provide an anatomic map of the body. Different medical imaging techniques may provide scans with complementary and occasionally conflicting information. The combination of images can often lead to additional clinical information not apparent in the separate images. The goal of image fusion is to impose a structural anatomic framework on functional images. Often in a functional image, there simply isn't enough anatomic detail to determine the position ofa tumor or other lesion. Although, the construction of a composite, overlapping medical image described in the field as medical image registration has been primarily used in the fusion offunctional and anatomical images, it has also been applied to a series of the same modality images. Examples of this are registration of SPECT images of the same subject in follow-up studies or in a comparison of Functional imaging using single photon emission computed tomography (SPECT) and positron emission tomography (PET) is extremely valuable in the diagnosis of various disorders. Uncertainty in the anatomic definition on SPECT and PET images, however, sometimes limits their usefulness. To overcome this problem, a combination ofmagnetic resonance images (MRI) and X-ray computed tomography (CT) images with functional SPECT or PET images of the same sections of the body, is used. This provides complementary anatomic (MRI or CT) and physiological (SPECT or PET) information that is of great importance to research, diagnosis, and treatment.

Multisensor Image Fusion Using the Wavelet Transform

The goal of image fusion is to integrate complementary information from multisensor data such that the new images are more suitable for the purpose of human visual perception and computer-processing tasks such as segmentation, feature extraction, and object recognition. This paper presents an image fusion scheme which is based on the wavelet transform. The wavelet transforms of the input images are appropriately combined, and the new image is obtained by taking the inverse wavelet transform of the fused wavelet coefficients. An area-based maximum selection rule and a consistency verification step are used for feature selection. The proposed scheme performs better than the Laplacian pyramid-based methods due to the compactness, directional selectivity, and orthogonality of the wavelet transform. A performance measure using specially generated test images is suggested and is used in the evaluation of different fusion methods, and in comparing the merits of different wavelet transform kernels. Extensive experimental results including the fusion of multifocus images, Landsat and Spot images, Landsat and Seasat SAR images, IR and visible images, and MRI and PET images are presented in the paper.