Wavelet-based Image Fusion Research Articles

Bounded PCA based Multi Sensor Image Fusion Employing Curvelet Transform Coefficients

The fusion of thermal and visible images acts as an important device for target detection. The quality of the spectral content of the fused image improves with wavelet-based image fusion. However, compared to PCA-based fusion, most wavelet-based methods provide results with a lower spatial resolution. The outcome gets better when the two approaches are combined, but they may still be refined. Compared to wavelets, the curvelet transforms more accurately depict the edges in the image. Enhancing the edges is a smart way to improve spatial resolution and the edges are crucial for interpreting the images. The fusion technique that utilizes curvelets enables the provision of additional data in both spectral and spatial areas concurrently. In this paper, we employ an amalgamation of Curvelet Transform and a Bounded PCA (CTBPCA) method to fuse thermal and visible images. To evidence the enhanced efficiency of our proposed technique, multiple evaluation metrics and comparisons with existing image merging methods are employed. Our approach outperforms others in both qualitative and quantitative analysis, except for runtime performance. Future Enhancement-The study will be based on using the fused image for target recognition. Future work should also focus on this method’s continued improvement and optimization for real-time video processing.

An efficient audio watermarking scheme with scrambled medical images for secure medical internet of things systems.

In recent times, the security of communication channels between healthcare entities in Medical Internet of Things (MIoT) systems becomes a significant issue to facilitate and guarantee the exchange of medical image and expertise securely. This paper presents an efficient audio watermarking scheme employing professionally Wavelet-based Image Fusion, Arnold transforms, and Singular Value Decomposition (SVD) for the secure transmission of medical images and reports in the MIoT applications. The essential consequence of the proposed scheme is to first syndicate two medical watermarks into a fused watermark to upsurge the payload of the inserted medical images. The fused watermark is then scrambled utilizing Arnold transform. Lastly, the Arnold fused watermark is inserted in the audio signal using the SVD algorithm following converting it into a 2D format. The choice of the Arnold transform for watermark is ascribed to settling robustness that skirmishes respective types of severe attacks. Several assessment metrics such as SNR, LLR, SNRseg, SD, and Cr are used to evaluate the audio watermarked signal and extracted watermarks The results reveal that the proposed audio watermarking scheme increases the capacity with more embedded medical images and security of implanted medical images transmission deprived of affecting the quality of audio signals, especially for IoT-based Telemedicine systems.

Wavelet-Based Multi-Focus Image Fusion Using Average Method Noise Diffusion (AMND)

Aim: This paper presents a new and upgraded wavelet-based multi-focus image fusion technique using average method noise diffusion (AMND). Objective: Improved visual appearance of the final image, no blurring in the final fused image and clearly visible objects (fine edges). Methods: This method extends the standard wavelet-based image fusion technique on multi-focus images by incorporating the hybrid of method noise and anisotropic diffusion in it. This hybrid structure of method noise and anisotropic diffusion is implemented as the post-processing operation in the proposed method. Results: The proposed work shows excellent results in terms of visual appearance and edge preservation. The experimental results of the proposed method are compared with some of the traditional and non-traditional approaches where the proposed method shows comparatively better results. Conclusion: This paper depicts the robustness, effectiveness and adaptive nature of method noise in the field of image enhancement especially in the field of image fusion. The performance of the proposed method is analyzed qualitatively (good visual appearance) and quantitatively (entropy, spatial frequency, and standard deviation). This method has the capability to be incorporated in real-time applications like surveillance in the visual sensor network (VSN).

Hydraulic fracture mapping using wavelet-based fusion of wave transmission and emission measurements

Fracture characterization is essential to hydrocarbon and geothermal exploration and production as well as carbon geo-sequestration. Active and passive measurements have been extensively applied to map fractures across various scales. Active and passive methods have inherent advantages and limitations that complement each other. In this study, to improve the mapping of embedded fractures and the surrounding geomechanically altered regions, we integrate active shear-wave transmission measurement with passive acoustic-emission measurement collected during a lab-scale hydraulic-fracturing experiment. The proposed data fusion leverages the advantages of the two modalities of measurements, while minimizing their individual limitations. Polarized shear-wave transmission (active) measurement was collected before and after the hydraulic fracturing, and the acoustic emission (passive) measurement was collected during hydraulic fracturing. Two sets of two-dimensional maps of fracture and fracture-induced damage in axial, median and frontal planes were obtained by separately processing active and passive measurements. These 2D maps are then fed to wavelet-based image fusion technique to integrate the two sources of information for the reliable mapping/imaging of the embedded fractures and the surrounding geomechanically altered regions.

An efficient wavelet-based image fusion for brain tumor detection and segmentation over PET and MRI image

A brain tumor is an abnormal growth of cells, reproducing themselves in an uncontrolled manner. In a medical diagnosis system, the accurate detection of location and size plays a very important role in the diagnosis of brain tumors. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are the most widely used techniques for diagnosis. The detection of a brain tumor in PET and MRI images is a challenging task due to its low sensitive boundary pixels. For accurate detection of brain tumors, in this paper, an efficient fusion-based brain tumor detection and segmentation is proposed. Here, at first, we fuse the input image using a discrete wavelet transform (DWT) and novel fusion rule. After the fusion process, the gray-level co-occurrence matrix (GLCM) features are extracted. Then, we categorize the brain images as normal and abnormal images using Optimal Deep Neural Network (ODNN). Here, the network weights of DNN are optimally selected using Spider Monkey Optimization (SMO) algorithm. After the classification process, the brain tumor region is extracted from abnormal brain images using the weighted k-means technique. The performance of the proposed methodology is analyzed in terms of sensitivity, specificity, and accuracy.

Predicting the Quality of View Synthesis With Color-Depth Image Fusion

With the increasing prevalence of free-viewpoint video applications, virtual view synthesis has attracted extensive attention. In view synthesis, a new viewpoint is generated from the input color and depth images with a depth-image-based rendering (DIBR) algorithm. Current quality evaluation models for view synthesis typically operate on the synthesized images, i.e. after the DIBR process, which is computationally expensive. So a natural question is that can we infer the quality of DIBR-based synthesized images using the input color and depth images directly without performing the intricate DIBR operation. With this motivation, this paper presents a no-reference image quality prediction model for view synthesis via COlor-Depth Image Fusion, dubbed CODIF, where the actual DIBR is not needed. First, object boundary regions are detected from the color image, and a Wavelet-based image fusion method is proposed to imitate the interaction between color and depth images during the DIBR process. Then statistical features of the interactional regions and natural regions are extracted from the fused color-depth image to portray the influences of distortions in color/depth images on the quality of synthesized views. Finally, all statistical features are utilized to learn the quality prediction model for view synthesis. Extensive experiments on public view synthesis databases demonstrate the advantages of the proposed metric in predicting the quality of view synthesis, and it even suppresses the state-of-the-art post-DIBR view synthesis quality metrics.

Autofocusing and image fusion for multi-focus plankton imaging by digital holographic microscopy.

Digital holographic microscopy is becoming increasingly useful for the analysis of marine plankton. In this study, we investigate autofocusing and image fusion in digital holographic microscopy. We propose an area metric autofocusing method and an improved wavelet-based image fusion method. In the area metric autofocusing method, a hologram image is initially segmented into several plankton regions for focus plane detection, and an area metric is then applied to these regions. In the improved wavelet-based image fusion method, a marked map is introduced for labeling each plankton region with the order of refocus plane images that accounts for the most pixels. The results indicate that the area metric autofocusing method applied to each plankton region provides a higher depth resolution accuracy than a number of general autofocusing methods, and the mean accuracy increases by approximately 33%. The improved wavelet-based image fusion method can fuse more than nine reconstructed plane images at a time and effectively eliminate fringes and speckle noise, and the fused image is much clearer than that of a general wavelet-based method, a sparse decomposition method, and a pulse-coupled neural networks method. This work has practical value for plankton imaging using digital holographic microscopy.

Ringing artifacts in wavelet based image fusion: Analysis, measurement and remedies

In this paper, we investigate an issue of the ringing artifacts inherent to wavelet based image fusion. A thorough analysis of the ringing phenomenon, by experimenting with different types of images and different wavelet families, with varying lengths of filters and varying levels of decomposition is performed to obtain deeper insights of the ringing artifacts. It is experimentally shown that wavelet based fusion results in the modification of the intra- and inter-scale dependencies, with the inter-scale dependency being the dominating factor causing the ringing artifacts. Also, these ringing artifacts are localized in the Fourier domain. Subsequently, a quantitative measure using structural dissimilarity is proposed to measure the ringing artifacts due to wavelet based fusion. Two possible solutions to compensate for the ringing artifacts are then proposed. In the first strategy, a filtering based method is proposed to reduce these ringing artifacts. It takes advantage of the localized nature of the ringing artifacts. Furthermore, the intra- and inter-scale dependencies are modeled using order-zero entropy. A second strategy using the inter-scale dependency is then proposed to reduce the ringing artifacts. Experimental results show that both these methods are able to reduce the ringing artifacts significantly and have further scope for improvement. Another critical finding of this work is selection of the wavelet filter and its levels of decomposition for the process of fusion.

Implementation and Performance Analysis of Pixel based and Wavelet based Image Fusion

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Curvelet Transform-based volume fusion for correcting signal loss artifacts in Time-of-Flight Magnetic Resonance Angiography data

Flow fields in cerebral aneurysms can be measured in vivo with phase-contrast MRI (4D Flow MRI), providing 3D anatomical magnitude images as well as 3-directional velocities through the cardiac cycle. The low spatial resolution of the 4D Flow MRI data, however, requires the images to be co-registered with higher resolution angiographic data for better segmentation of the blood vessel geometries to adequately quantify relevant flow descriptors such as wall shear stress or flow residence time. Time-of-Flight Magnetic Resonance Angiography (TOF MRA) is a non-invasive technique for visualizing blood vessels without the need to administer contrast agent. Instead TOF uses the blood flow-related enhancement of unsaturated spins entering into an imaging slice as means to generate contrast between the stationary tissue and the moving blood. Because of the higher resolutions, TOF data are often used to assist with the segmentation process needed for the flow analysis and Computational Fluid Dynamics (CFD) modeling. However, presence of slow moving and recirculating blood flow such as in brain aneurysms, especially regions where the blood flow is not perpendicular to the image plane, causes signal loss in these regions. In this work a 3D Curvelet Transform-based image fusion approach is proposed for signal loss artifact reduction of TOF volume data. Experiments show the superiority of the proposed approach in comparison to other multi-resolution 3D Wavelet-based image fusion methodologies. The proposed approach can further facilitate model-based fluid analysis and pre/post-operative treatment of patients with brain aneurysms.

Image enhancement with contrast coefficients using wavelet based image fusion

The future is mainly focused on image brightness and the capacity that required storing the image. The sharp images provide better information than the blur images. To overcome from the blurriness in the image, we use image enhancement techniques. Image fusion used to overcome information loss in the image. This paper is provided with image enhancement and fusion by applying wavelet transform technique. Wavelet transform is mainly used because due to its inherent property that is they are redundant and shift invariant. It transforms the image into different scales. Image enhancement will be decided based on the levels of transformation. Low contrast results from poor resolution, lack of dynamic range, wrong settings of sensor lens during acquisition and poor quality of cameras and sensors. To avoid the information loss there is an interesting solution that is for the pictures of the same image but focused on different regions. Then using image fusion concept, all images which are captured are combined to get a single image which contains the properties of both the source images. The image entropy is composed to determine the quality of the image. The paper shows the image fusion method for both multi-resolution and images captured at different temperatures.

Design and Development of IP for Modified Haar Wavelet Transform (MHWT) Image Fusion using FPGA

<p>The fast growth in the field of digital imaging applications in remote sensing, bio medical and other satellite applications had created an architecture studies for image fusion in capable to store large amount of data and process. An algorithm considered for the process of image fusion for implementation of FPGA is Modified Haar Wavelet Transform (MHWT) based image fusion where at the time four pixels are consider in calculation of different bands as compared to conventional Haar wavelet based image fusion. The process of modification uses far less memory and computation power. The FPGA implementation of MHWT based image fusion is done on Digilent development board with Spartan 6 series FPGA. The architecture is developed in VHDL. The timing analysis is done and report is obtained for I/O interactions, memory units etc. The architecture is made to run in cosimulation with Simulink. The design is tested with different kinds of images and run successfully. The visual analysis of the resultant fused image is achived and observed.</p>

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms.

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms. A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details. The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations. In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

Review of Medical Image Enhancement using wavelet based Image Fusion

Review of Medical Image Enhancement using wavelet based Image Fusion

Processing and fusion of passively acquired, millimeter and terahertz images of the human body

A passive, millimeter wave (MMW) and terahertz (THz) dual-band imaging system composed of 94 and 250 GHz single-element detectors was used to investigate preprocessing and fusion algorithms for dual-band images. Subsequently, an MMW and THz image preprocessing and fusion integrated algorithm (MMW-THz IPFIA) was developed. In the algorithm, a block-matching and three-dimensional filtering denoising algorithm is employed to filter noise, an adaptive histogram equalization algorithm to enhance images, an intensity-based registration algorithm to register images, and a wavelet-based image fusion algorithm to fuse the preprocessed images. The performance of the algorithm was analyzed by calculating the SNR and information entropy of the actual images. This algorithm effectively reduces the image noise and improves the level of detail in the images. Since the algorithm improves the performance of the investigated imaging system, it should support practical technological applications. Because the system responds to blackbody radiation, its improvement is quantified herein using the static performance parameter commonly employed for thermal imaging systems, namely, the minimum detectable temperature difference (MDTD). An experiment was conducted in which the system’s MDTD was measured before and after applying the MMW-THz IPFIA, verifying the improved performance that can be realized through its application.

3-D wavelet-based fusion approach for comprehensively analyzing multiple physical-property voxel models inverted from potential-field data

The fusion technique is a simple and fast but useful tool for integrated interpretation of various types of geophysical data. Based on the routine 2-D wavelet-based image fusion approach, we presented a 3-D wavelet-based fusion approach for comprehensively analyzing various physical-property voxel models inverted from gravity and magnetic data. The approach decomposes the models via the 3-D discrete wavelet transformation, and then fuses them according to the separated fusion rules of the approximated and detailed components in the wavelet domain, and then reconstructs the fused model via the inverse 3-D discrete wavelet transformation. The related parameters of the approach include the wavelet basis, the layer number of decomposition, the weighted coefficients for fusing the approximated components, and the window size for fusing the detailed components. Tests on the synthetic data and the real data from a metallic deposit area in Northwest China verified feasibility of the 3-D fusion approach.

An Investigative Approach and Analysis on Fusion Techniques of Images for Medical Beneficial Applications

Objectives: Image fusion is the practice of combining appropriate data in sequence from a position of images into a distinct image and the fused or combined image contains supplementary information than any of the contributed image. Methods: The most important methods of the image fusion engages the pyramid based image fusion, simple image fusion, and wavelet based image fusion. To compute the quality and excellence of images is for purpose of evaluation of image fusion performance measures of Entropy, Peak signal to Noise ratio ,Correlation Coefficient(CC), RMS inaccuracy, SD(Standard Deviation), Edge Detection which is considered , High Pass Correlation of Image, Average Gradient of image has been introduced. Entropy is for the determination of data informational quantity, Peak signal to Noise ratio is for the evaluation of image error, Correlation Coefficient is utilized to come across with the similarities connecting the contributed and the fused complex image, RMS inaccuracy is collective noise sandwiched between the fused and the innovative input image Findings: In this research paper an analysis is done on images with the approach of image fusion techniques of wavelet transform and focuses on their assessment and evaluation based on the superiority of the harvested or produced image. Conclusion: The outputs are verified that performance in terms of lesser entropy and greater PSNR gives common sequence of information. Here SWT displays good performance and high-quality performance is always obtained by using wavelet transform. Wavelet transform has enhanced capability to recognize the border path feature and superior Medical Image analysis.

A Comparative Study of Wavelet-Based Image Fusion with a Novel Fusion Rule

A Comparative Study of Wavelet-Based Image Fusion with a Novel Fusion Rule

Enhanced disease characterization through multi network functional normalization in fMRI.

Conventionally, structural topology is used for spatial normalization during the pre-processing of fMRI. The co-existence of multiple intrinsic networks which can be detected in the resting brain are well-studied. Also, these networks exhibit temporal and spatial modulation during cognitive task vs. rest which shows the existence of common spatial excitation patterns between these identified networks. Previous work (Khullar et al., 2011) has shown that structural and functional data may not have direct one-to-one correspondence and functional activation patterns in a well-defined structural region can vary across subjects even for a well-defined functional task. The results of this study and the existence of the neural activity patterns in multiple networks motivates us to investigate multiple resting-state networks as a single fusion template for functional normalization for multi groups of subjects. We extend the previous approach (Khullar et al., 2011) by co-registering multi group of subjects (healthy control and schizophrenia patients) and by utilizing multiple resting-state networks (instead of just one) as a single fusion template for functional normalization. In this paper we describe the initial steps toward using multiple resting-state networks as a single fusion template for functional normalization. A simple wavelet-based image fusion approach is presented in order to evaluate the feasibility of combining multiple functional networks. Our results showed improvements in both the significance of group statistics (healthy control and schizophrenia patients) and the spatial extent of activation when a multiple resting-state network applied as a single fusion template for functional normalization after the conventional structural normalization. Also, our results provided evidence that the improvement in significance of group statistics lead to better accuracy results for classification of healthy controls and schizophrenia patients.

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.