Image fusion

Today's research era, image fusion is a actual step by step procedure to develop the visualization of any image. It integrates the essential features of more than a couple of images into a individual fused image without taking any artifacts. Multifocus image fusion has a vital key factor in fusion process where it aims to increase the depth of field using extracting focused part from different multiple focused images. In this paper multi-focus image fusion algorithm is proposed where non local mean technique is used in stationary wavelet transform (SWT) to get the sharp and smooth image. Non-local mean function analyses the pixels belonging to the blurring part and improves the image quality. The proposed work is compared with some existing methods. The results are analyzed visually as well as using performance metrics.

To study the impact of fused (18)F-fluoro-deoxy-D-glucose (FDG)-hybrid positron emission tomography (PET) and computed tomography (CT) images on conformal radiation therapy (CRT) planning for patients with esophageal carcinoma. Thirty-four patients with esophageal carcinoma were referred for concomitant radiotherapy and chemotherapy with radical intent. Each patient underwent CT and FDG-hybrid PET for simulation treatment in the same radiation treatment position. PET-images were coregistered using five fiducial markers. Target delineation was initially performed on CT images and the corresponding PET data were subsequently used as an overlay to CT data to define the target volume. FDG-PET identified previously undetected distant metastatic disease in 2 patients, making them ineligible for curative CRT. The Gross Tumor Volume (GTV) was decreased by CT and FDG image fusion in 12 patients (35%) and was increased in 7 patients (20.5%). The GTV reduction was >or=25% in 4 patients due ...

—Hough transform (HT) is a typical method to detect or segment geometry objects from images. In this paper, we study the principle of Hough Transform and its mathematical expressions, and try to use a new approach based on Hough transform for quick line and circle detection in image processing. Our method accurately detected some simple graphics, such as straight line of different direction, circles of different detection, thickness and different number. The results show that our method is less memory consumption and calculated fast, which could be applied for line detection and segmentation in 3D ultrasonic image.

This study compared four image fusion techniques for mangrove studies in the Beilun estuary, Vietnam. The fused image was generated from an ALOS multi-spectral image scene and a Quickbird panchromatic image scene. Four quantitative indices (i.e. mean, standard deviation, entropy, and correlation coefficient) were used to compare and evaluate the results of the fusion methods. Variation of fusion effects was observed among different methods. In addition, fused images were used to identify mangrove biomass levels via spectral mixture analysis. The fusion results are not quite very well from a series of evaluations, two possible explanations of the failure results present in the conclusion, which provide the reference for the relevant research.

... MU-WEI JIAN1, JUN-YU DONG1, JIA-HUA (JERRY) WU 2 ... 2 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton Cambridge, United Kingdom E-MAIL: dongjunyu@ouc.edu.cn, jianmuwei@sohu.com, jerry.wu@sanger ... [5] Jian, Muwei; Dong, Junyu; ...

Comparison of image processing techniques is critically important in deciding which algorithm, method, or metric to use for enhanced image assessment. Image fusion is a popular choice for various image enhancement applications such as overlay of two image products, refinement of image resolutions for alignment, and image combination for feature extraction and target recognition. Since image fusion is used in many geospatial and night vision applications, it is important to understand these techniques and provide a comparative study of the methods. In this paper, we conduct a comparative study on 12 selected image fusion metrics over six multiresolution image fusion algorithms for two different fusion schemes and input images with distortion. The analysis can be applied
to different image combination algorithms, image processing methods, and over a different choice of metrics that are of use to an image processing expert. The paper relates the results to an image quality measurement based on power spectrum and correlation analysis and serves as a summary of many contemporary techniques for objective assessment of image fusion algorithms.

The ExacTrac X-Ray 6D image-guided radiotherapy (IGRT) system will be described and its performance evaluated. The system is mainly an integration of 2 subsystems: (1) an infrared (IR)-based optical positioning system (ExacTrac) and (2) a radiographic kV x-ray imaging system (X-Ray 6D). The infrared system consists of 2 IR cameras, which are used to monitor reflective body markers placed on the patient's skin to assist in patient initial setup, and an IR reflective reference star, which is attached to the treatment couch and can assist in couch movement with spatial resolution to better than 0.3 mm. The radiographic kV devices consist of 2 oblique x-ray imagers to obtain high-quality radiographs for patient position verification and adjustment. The position verification is made by fusing the radiographs with the simulation CT images using either 3 degree-of-freedom (3D) or 6 degree-of-freedom (6D) fusion algorithms. The position adjustment is performed using the infrared system ...

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.

To create a quality output image by fusing, the features from both infrared and visible image for target detection in military applications. Image fusion is the amalgamation of two or more images of the same scene taken at different times, different focal length or at different sensors. The proposed work includes hybrid image fusion in transform domain by the combination of DWT (Discrete Wavelet Transform) and LWT (Lifting Wavelet Transform) and SWT (Shearlet Wavelet Transform). The source images are first decomposed using DWT and the low pass information is further decomposed by LWT or SWT and fused by weighted sum method and the high pass information is fused by max method. The performance evaluation metrics such as PSNR, Edge association, Entropy, Root Mean squared error, Correlation coefficient, Structural Similarity Index are used to compare the fused result of DWT, LWT, SWT and hybrid combination of DWT, LWT and DWT, SWT both quantitatively and qualitatively. Among the results hybrid combination of DWT and LWT performs better.

The state-of-the-art diagnostic tools in oral and maxillofacial surgery and preoperative orthodontic treatment are mainly two-dimensional, and consequently reveal limitations in describing the three-dimensional (3D) structures of a patient's face. New 3D imaging techniques, such as 3D stereophotogrammetry (3D photograph) and cone-beam computed tomography (CBCT), have been introduced. Image fusion, i.e. registration of a 3D photograph upon a CBCT, results in an accurate and photorealistic digital 3D data set of a patient's face. The purpose of this study was to determine the accuracy of three different matching procedures. For 15 individuals the textured skin surface (3D photograph) and untextured skin surface (CBCT) were matched by two observers using three different methods to determine the accuracy of registration. The registration error was computed as the difference (mm) between all points of both surfaces. The registration errors were relatively large at the lateral nec...

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.

Due to the constant development of new and better imaging sensors which operate according to different physical principles, the need arises for a meaningful combination of all these imaging data. For many years, various research fields have been trying to integrate imagery of different modalities – not at least in the medical and geoscience communities – to facilitate a better understanding and interpretation of particular phenomena. This topic is also becoming increasingly applicable in archaeology, as imagery and data sources become more diverse and complex.

In order to implement such an approach, a dedicated MATLAB toolbox TAIFU (the Toolbox for Archaeological Image Fusion) has been created. TAIFU serves as a platform for testing of current state-of-the-art image fusion methods and facilitates the development of new data integration routines. This toolbox is thus designed to benefit archaeological interpretive mapping of diverse prospection datasets. For example, the data from a magnetogram can be fused with an aerial image to aid the archaeologist in correlating feature locations for a more trustworthy information extraction and better interpretation of hidden geo-cultural features.

TAIFU is currently programmed in such a way that the user has to load two images. Upon import, the toolbox verifies and stores the metadata of the images (such as georeferencing, Exif and IPTC tags). Subsequently, a variety of image fusion processes (some with additional options) can be chosen to create a fused image from the initial displayed images. As a pre- or post-fusion step, the user can also choose among a variety of contrast enhancement algorithms. When a useful result is obtained, the fused image can be saved with all the proper metadata embedded, although the latter can also be stored as a sidecar ASCII file. These new metadata do not only originate from both input images, they also contain data about the contrast enhancement and fusion algorithms that were used to obtain the final result.

At this stage, TAIFU is still in development. So far, about thirty image fusion methods have been implemented next to two different image fusion metrics. Although a visual assessment of the result will normally determine which method is best suited for a specific case, these metrics can help to identify which fusion approaches preserve most information of both input images.

Since a few years, structure-from-motion and multi-view stereo pipelines have become omnipresent in the cultural heritage domain. The fact that such Image-Based Modelling (IBM) approaches are capable of providing a photo-realistic texture along the three-dimensional (3D) digital surface geometry is often considered a unique selling point, certainly for those cases that aim for a visually pleasing result. However, this texture can very often also obscure the underlying geometrical details of the surface, making it very hard to assess the morphological features of the digitised artefact or scene. Instead of constantly switching between the textured and untextured version of the 3D surface model, this paper presents a new method to generate a morphology-enhanced colour texture for the 3D polymesh. The presented approach tries to overcome this switching between objects visualisations by fusing the original colour texture data with a specific depiction of the surface normals. Whether applied to the original 3D surface model or a low-resolution derivative, this newly generated texture does not solely convey the colours in a proper way but also enhances the small-and large-scale spatial and morphological features that are hard or impossible to perceive in the original textured model. In addition, the technique is very useful for low-end 3D viewers, since no additional memory and computing capacity are needed to convey relief details properly. Apart from simple visualisation purposes, the textured 3D models are now also better suited for on-surface interpretative mapping and the generation of line drawings.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.

It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives.