Fusion Of Multiple Images Research Articles

Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet trans- form. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JEI.25.1.013007)

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multiscale Self-coordination of Bidimensional Empirical Mode Decomposition in Image Fusion

The bidimensional empirical mode decomposition (BEMD) algorithm with high adaptability is more suitable to process multiple image fusion than traditional image fusion. However, the advantages of this algorithm are limited by the end effects problem, multiscale integration problem and number difference of intrinsic mode functions in multiple images decomposition. This study proposes the multiscale self-coordination BEMD algorithm to solve this problem. This algorithm outside extending the feather information with the support vector machine which has a high degree of generalization, then it also overcomes the BEMD end effects problem with conventional mirror extension methods of data processing, . The coordination of the extreme value point of the source image helps solve the problem of multiscale information fusion. Results show that the proposed method is better than the wavelet and NSCT method in retaining the characteristics of the source image information and the details of the mutation information inherited from the source image and in significantly improving the signal-to-noise ratio.

Multi-resolution wavelet-based image fusion for iris recognition

Iris recognition is one of the most powerful techniques for biometric identification. The requirement of current scenario is to have a simple and efficient scheme for iris recognition with high performance of the system. Existing methods suffer from some undesirable side effects and reduced feature contrast which degrades the quality of the output image. Furthermore, some of these methods are rather complex and this contradicts the concept of the simplicity. Image fusion is an important tool for improving performance in image-based applications such as remote sensing, machine vision, medical imaging and so on. In this paper, an efficient approach for fusion of multiple iris images based on multi-resolution wavelet is presented. Root mean-square error (RMSE) and correlation coefficient (CORR) are used as the assessment metrics for evaluation. The algorithm reduces the elapsed time and accelerates the verification process with high recognition accuracy. The Chinese Academy of Sciences - Institute of Automation (CASIA) iris database is used to simulate the studies. The approach used in the proposed work outperforms existing approaches with the fact that in the proposed iris recognition system, the feature level method preserves the information from the edges.

SU‐E‐I‐23: Design and Clinical Application of External Marking Body in Multi‐ Mode Medical Images Registration and Fusion

Purpose:To design an external marking body (EMB) that could be visible on computed tomography (CT), magnetic resonance (MR), positron emission tomography (PET) and single‐photon emission computed tomography (SPECT) images and to investigate the use of the EMB for multiple medical images registration and fusion in the clinic.Methods:We generated a solution containing paramagnetic metal ions and iodide ions (CT' MR dual‐visible solution) that could be viewed on CT and MR images and multi‐mode image visible solution (MIVS) that could be obtained by mixing radioactive nuclear material. A globular plastic theca (diameter: 3–6 mm) that mothball the MIVS and the EMB was brought by filling MIVS. The EMBs were fixed on the patient surface and CT, MR, PET and SPECT scans were obtained. The feasibility of clinical application and the display and registration error of EMB among different image modalities were investigated.Results:The dual‐visible solution was highly dense on CT images (HU>700). A high signal was also found in all MR scanning (T1, T2, STIR and FLAIR) images, and the signal was higher than subcutaneous fat. EMB with radioactive nuclear material caused a radionuclide concentration area on PET and SPECT images, and the signal of EMB was similar to or higher than tumor signals. The theca with MIVS was clearly visible on all the images without artifact, and the shape was round or oval with a sharp edge. The maximum diameter display error was 0.3 ± 0.2mm on CT and MRI images, and 1.0 ± 0.3mm on PET and SPECT images. In addition, the registration accuracy of the theca center among multi‐mode images was less than 1mm.Conclusion:The application of EMB with MIVS improves the registration and fusion accuracy of multi‐mode medical images. Furthermore, it has the potential to ameliorate disease diagnosis and treatment outcome.

Implementation of Land-sat Image Fusion FPGA based Hardware Using Wavelet Transform

Image fusion is the fusion process of combining two or more images into a single image by considering the important features from each real image. Lot of approaches was there to achieve Image fusion. The images are transformed in the spatial or another domain so that images are fused. Therefore Wavelet Transform is the best method. There are 54 wavelet types, 9 rules of fused are compared and analyzed in multiple image fusion process. The fused image produces better quality equal to the separate images. Similarity Measures is the correlation of the fused images. These measures provide a quantitative measure of the degree of match between two images or image patches. Image similarity measures play an important role in applications including retrieval, change detection, classification, registration and quality evaluation and many image fusion algorithms. DWT can be interpreted as signal decomposition in a spatially oriented frequency channel. The signal S passed through two complementary filters and emerges as two signals, Details and approximation. This is called as analysis or decomposition.

Ghost-Free High Dynamic Range Imaging Using Histogram Separation and Edge Preserving Denoising

In this paper, we introduce a ghost-free High Dynamic Range imaging algorithm for obtaining ghost-free high dynamicrange (HDR) images. The multiple image fusion based HDR method work only on condition that there is no movement ofcamera and object when capturing multiple, differently exposed low dynamic range (LDR) images. The proposed algorithmmakes three LDR images from a single input image to remove such an unrealistic condition. For this purpose a histogramseparation method is proposed in the algorithm for generating three LDR images by stretching each separated histogram.An edge-preserving denoising technique is also proposed in the algorithm to suppress the noise that is amplified in thestretching process. In the proposed algorithm final HDR image free from ghost artifacts in dynamic environment because itself-generates three LDR images from a single input image. Therefore, the proposed algorithm can be use in mobilephone camera and a consumer compact camera to provide the ghost artifacts free HDR images in the form of either inbuiltor post-processing software application.

Synchronized 2D/3D optical mapping for interactive exploration and real-time visualization of multi-function neurological images

Efficient software with the ability to display multiple neurological image datasets simultaneously with full real-time interactivity is critical for brain disease diagnosis and image-guided planning. In this paper, we describe the creation and function of a new comprehensive software platform that integrates novel algorithms and functions for multiple medical image visualization, processing, and manipulation. We implement an opacity-adjustment algorithm to build 2D lookup tables for multiple slice image display and fusion, which achieves a better visual result than those of using VTK-based methods. We also develop a new real-time 2D and 3D data synchronization scheme for multi-function MR volume and slice image optical mapping and rendering simultaneously through using the same adjustment operation. All these methodologies are integrated into our software framework to provide users with an efficient tool for flexibly, intuitively, and rapidly exploring and analyzing the functional and anatomical MR neurological data. Finally, we validate our new techniques and software platform with visual analysis and task-specific user studies.

Image Segmentations for Through-the-Wall Radar Target Detection

Detection of stationary targets using pixel-wise likelihood ratio test (LRT) detectors has been recently proposed for through-the-wall radar imaging (TWRI) applications. We employ image segmentation techniques, in lieu of LRT, for target detection in TWRI. More specifically the widely used between-class variance (BCV) thresholding, maximum entropy segmentation, and K-means clustering are considered to aid in removing the clutter, resulting in enhanced radar images with target regions only. For the case when multiple images of the same scene are available through diversity in polarization and/or vantage points around a building structure, we propose to use image fusion, following the image segmentation step, to generate an enhanced composite image. In particular, additive, multiplicative, and fuzzy logic fusion techniques are considered. The performance of the segmentation and fusion schemes is evaluated and compared with that of the assumed LRT detector using both electromagnetic (EM) modeling and real data collected in a laboratory environment. The results show that, although the principles of segmentation and detection are different, the segmentation techniques provide either comparable or improved performance over the LRT detector. Specifically, in the cases considered, the maximum entropy segmentation produces the best results for detection of targets inside building structures. For fusion of multiple segmented images of the same scene, the fuzzy logic fusion outperforms the other methods.

Assessing the performance of a method of simultaneous compression and encryption of multiple images and its resistance against various attacks

We introduce a double optimization procedure for spectrally multiplexing multiple images. This technique is adapted from a recently proposed optical setup implementing the discrete cosine transformation (DCT). The new analysis technique is a combination of spectral fusion based on the properties of DCT, specific spectral filtering, and quantization of the remaining encoded frequencies using an optimal number of bits. Spectrally multiplexing multiple images defines a first level of encryption. A second level of encryption based on a real key image is used to reinforce encryption. A set of numerical simulations and a comparison with the well known JPEG (Joint Photographic Experts Group) image compression standard have been carried out to demonstrate the improved performances of this method. The focus here will differ from the method of simultaneous fusion, compression, and encryption of multiple images (SFCE) [Opt. Express 19, 24023 (2011)] in the following ways. Firstly, we shall be concerned with optimizing the compression rate by adapting the size of the spectral block to each target image and decreasing the number of bits required to encode each block. This size adaptation is achieved by means of the root-mean-square (RMS) time-frequency criterion. We found that this size adaptation provides a good tradeoff between bandwidth of spectral plane and number of reconstructed output images. Secondly, the encryption rate is improved by using a real biometric key and randomly changing the rotation angle of each block before spectral fusion. By using a real-valued key image we have been able to increase the compression rate of 50% over the original SFCE method. We provide numerical examples of the effects for size, rotation, and shifting of DCT-blocks which play noteworthy roles in the optimization of the bandwidth of the spectral plane. Inspection of the results for different types of attack demonstrates the robustness of our procedure.

A non-diffracting image restoration based on multiple images fusion

Axicon, a useful optical element for generating non-diffracting beams, can extend the depth of field of imaging system, and the acquired images are insensitive to out of focus. However the images are not clear as well as low contrast. By analyzing the imaging characteristics of the non-diffracting imaging system and the properties of the point spread function, we present an image restoration method of multiple images fusion. It can reduce the influence of random noise and improve the high frequency part of the image. Experimental results show that this method can effectively enhance image detail and realize clear imaging within the large depth of field.

A Novel Image Denoising Method for Matched Filtering System

One method is proposed to remove the random noise and low-frequency coherent noise in the images of the optic 4f system, which is based on fusion of multiple spatial frequency spectrum images. The multiple spatial frequency spectrum images of once experiment are captured based on the image copying character from the lattice structure of spatial light modulators, which contains the same useful image information and noise with the similar distribution but different values. The random noise and coherent noise in these images are removed by combining it utilizing image fusion technique, which is similar to cumulative mean in time domain. The theoretical analysis and physical experiment suggests that the method is able to remove random noise and low-frequency coherent noise effectively and reserve the useful image information.

Adopting and Implementation of Self Organizing Feature Map for Image Fusion

A different image fusion algorithm based on self organizing feature map is proposed in this paper, aiming to produce quality images. Image Fusion is to integrate complementary and redundant information from multiple images of the same scene to create a single composite image that contains all the important features of the original images. The resulting fused image will thus be more suitable for human and machine perception or for further image processing tasks. The existing fusion techniques based on either direct operation on pixels or segments fail to produce fused images of the required quality and are mostly application based. The existing segmentation algorithms become complicated and time consuming when multiple images are to be fused. A new method of segmenting and fusion of gray scale images adopting Self organizing Feature Maps(SOM) is proposed in this paper. The Self Organizing Feature Maps is adopted to produce multiple slices of the source and reference images based on various combination of gray scale and can dynamically fused depending on the application. The proposed technique is adopted and analyzed for fusion of multiple images. The technique is robust in the sense that there will be no loss in information due to the property of Self Organizing Feature Maps; noise removal in the source images done during processing stage and fusion of multiple images is dynamically done to get the desired results. Experimental results demonstrate that, for the quality multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities.

Objective Quality Assessment of Tone-Mapped Images

Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Different TMOs create different tone-mapped images, and a natural question is which one has the best quality. Without an appropriate quality measure, different TMOs cannot be compared, and further improvement is directionless. Subjective rating may be a reliable evaluation method, but it is expensive and time consuming, and more importantly, is difficult to be embedded into optimization frameworks. Here we propose an objective quality assessment algorithm for tone-mapped images by combining: 1) a multiscale signal fidelity measure on the basis of a modified structural similarity index and 2) a naturalness measure on the basis of intensity statistics of natural images. Validations using independent subject-rated image databases show good correlations between subjective ranking score and the proposed tone-mapped image quality index (TMQI). Furthermore, we demonstrate the extended applications of TMQI using two examples-parameter tuning for TMOs and adaptive fusion of multiple tone-mapped images.

Accuracy study of new computer-assisted orthopedic surgery software

PurposeThe new computerized system is based on image analysis and designed to aid in orthopedic surgeries by virtual trajectory of the guide wire, intra-operative planning and various measurements. Validation of the accuracy and safety of any computer-aided surgery system is essential before implementing it clinically. We examined the accuracy of guide-wire length and angle measurements and fusion of multiple adjacent images (panoramic view image, PVI®) of the new software. MethodsThis is a 2-part study. Part I: twenty guide wires were drilled to various depths in a synthetic femur model and the results obtained by the software measurements were compared with manual measurements by a caliper and a depth gauge. Part II: a sawbone femur shaft was osteotomized and various inclinations of >10° to the varus or valgus angles were tested. The manually obtained measurements of angles and lengths were compared to the new computerized system software PVI. ResultsThere was a significant positive linear correlation between all groups of the computerized length and the control measurements (r>0.983, p<0.01). There was no significant difference among different distances, angles or positions from the image intensifier. There was a significant positive linear correlation between the angle and length measurement on the PVI and the control measurement (r>0.993, p<0.01). ConclusionsThe new computerized software has high reliability in performing measurements of length using an aiming, positioning and referring device intra-operatively.

Simultaneous fusion, compression, and encryption of multiple images

We report a new spectral multiple image fusion analysis based on the discrete cosine transform (DCT) and a specific spectral filtering method. In order to decrease the size of the multiplexed file, we suggest a procedure of compression which is based on an adapted spectral quantization. Each frequency is encoded with an optimized number of bits according its importance and its position in the DC domain. This fusion and compression scheme constitutes a first level of encryption. A supplementary level of encryption is realized by making use of biometric information. We consider several implementations of this analysis by experimenting with sequences of gray scale images. To quantify the performance of our method we calculate the MSE (mean squared error) and the PSNR (peak signal to noise ratio). Our results consistently improve performances compared to the well-known JPEG image compression standard and provide a viable solution for simultaneous compression and encryption of multiple images.

Multiple Exposure Fusion for High Dynamic Range Image Acquisition

A multiple exposure fusion to enhance the dynamic range of an image is proposed. The construction of high dynamic range images (HDRIs) is performed by combining multiple images taken with different exposures and estimating the irradiance value for each pixel. This is a common process for HDRI acquisition. During this process, displacements of the images caused by object movements often yield motion blur and ghosting artifacts. To address the problem, this paper presents an efficient and accurate multiple exposure fusion technique for the HDRI acquisition. Our method simultaneously estimates displacements and occlusion and saturation regions by using maximum a posteriori estimation and constructs motion-blur-free HDRIs. We also propose a new weighting scheme for the multiple image fusion. We demonstrate that our HDRI acquisition algorithm is accurate, even for images with large motion.

Multiple Input to Multiple Output Images Fusion Based on Turbo Iteration

This paper mainly addresses the problem of multipolar Synthetic Aperture Radar (SAR) and colorful optical images fusion by regarding them as multichannel images. Based on traditional wavelet-based and model-based fusion algorithms, the paper proposes a multi-channel image fusion algorithm based on a multi-multiturbo iterative method. Multi-multiframe is proposed to represent the original image information with multiple outputs from better information-separating viewpoints, and turbo iterative balances wavelet-based and model-based fusion. The approach is designed in this manner. First, Intensity-Hue-Saturation (IHS) transformation is applied to the SAR and optical images. Then, different fusion processes are used on corresponding components. Fusion based on multi-multi and turbo iterative is applied to the Intensity component whereas weighted fusion is applied to Hue and Saturation components. To get the final result, inverse IHS transformation is applied. Experimental results show that the proposed algorithm performs effectively in preserving useful complementary information between optical and SAR images.

Multifocus image fusion using modified pulse coupled neural network for improved image quality

The present day camera systems have the limitation of acquiring the clearer image of a scene having objects at different distances. This limitation can be overcome by fusion of multiple images of the scene taken with different camera settings. The fusion of these images comes under the category of multifocus image fusion. In the existing method of image, fusion partitioned source image blocks are fused by pulse coupled neural network (PCNN) based on their clarity measure. PCNN plays an important role in the image fusion process in choosing the best-quality image block for fused image. Fusion method becomes tedious and time consuming because of the inherent complexity of PCNN. In this study, a modified approach of PCNN suitable for application in image fusion technique is proposed by reducing the processing time and computational complexity. The modifications proposed are in linking and feeding field of PCNN. This study presents a method for multifocus image fusion by using modified PCNN (MPCNN) with spatial frequency (SF) and energy of Laplacian (EOL) as clarity measures. The proposed method of image fusion using MPCNN results in better quality of fused image with reduced root mean square error (RMSE) and computational time requirements as compared to conventional PCNN.

Visualisation of the subthalamic nucleus: a multiple sequential image fusion (MuSIF) technique for direct stereotaxic localisation and postoperative control.

A novel multiple, sequential image fusion (MuSIF) procedure merging stereotaxic CT with frameless magnetic resonance imaging (MRI) is used since June 2000 to visualise and directly localise the subthalamic nucleus (STN) on T2 images. In 13 consecutive Parkinson's cases, intraoperative recording and stimulation verified bilateral electrode implantation guided by fused T2 images. In 85% of sides, final implantation opted for visualised target track. Implanted electrode position on postoperative T2 images matched planned target. Clinical follow-up reproduces literature's best results. This MuSIF technique, effective for direct STN targeting, has practical advantages: MRI can be performed regardless of surgery time; regular MR scanning to correct real image distortion is unneeded; and the need for multiple localising tracks is reduced by enabling us to account for each patient's STN anatomy.