An efficient image fusion method based on dual tree complex wavelet transform

To increase the resolution of any image, interpolation techniques are adopted. The high frequency components in the low resolution (LR) image are lost when the images are interpolated. To overcome this problem a new satellite image resolution enhancement algorithm based on Dual Tree Complex Wavelet transform (DTCWT) and its rotated version have been proposed. DTCWT and Rotated DTCWT give 32 subbands of an image, out of which 24 are high frequency (HF) subbands which give 12 different angular information and 8 are low frequency (LF) subbands. The HF subbands are interpolated by Lanczos Interpolation to preserve the high frequency contents of the image. Non Local Means (NLM) filtering is used to eliminate the artifacts which are generated by DTCWT and rotated DTCWT. To obtain the two enhanced high resolution images inverse transforms are performed over respective subbands. The final two high resolution (HR) images are fused together with DTCWT based fusion to give resolution enhanced HR image. To evaluate the performance of the proposed algorithm three performance parameters namely PSNR, SSIM and Q-Index are evaluated for a database of 60 grayscale images of resolution 256×256. The subjective and objective results are compared with the existing techniques to prove the superiority of the proposed algorithm.

Background: During the two past decades, medical image fusion has become an essential part ofmodern medicine due to the availability of numerous imaging modalities (e.g., MRI, CT, SPECT,etc.). This paper presents a new medical image fusion algorithm based on PCA and DTCWT,which uses different fusion rules to obtain a new image containing more information than any ofthe input images.Methods: A new image fusion algorithm improves the visual quality of the fused image, based onfeature selection and Principal Component Analysis (PCA) in the Dual-Tree Complex WaveletTransform (DTCWT) domain. It is called Feature Selection with Principal Component Analysisand Dual-Tree Complex Wavelet Transform (FSPCA-DTCWT). Using different fusion rules in asingle algorithm result in correctly reconstructed image (fused image), this combination willproduce a new technique, which employs the advantages of each method separately. The DTCWTpresents good directionality since it considers the edge information in six directions and providesapproximate shift invariant. The main goal of PCA is to extract the most significant characteristics(represented by the wavelet coefficients) in order to improve the spatial resolution. The proposedalgorithm fuses the detailed wavelet coefficients of input images using features selection rule.Results: Several experiments have been conducted over different sets of multimodal medicalimages such as CT/MRI, MRA/T1-MRI. However, due to pages-limit on a paper, only results ofthree sets have been presented. The FSPCA-DTCWT algorithm is compared to recent fusionmethods presented in the literature (eight methods) in terms of visual quality and quantitativelyusing well-known fusion performance metrics (five metrics). Results showed that the proposedalgorithm outperforms the existing ones regarding visual and quantitative evaluations.Conclusion: This paper focuses on medical image fusion of different modalities. A novel imagefusion algorithm based on DTCWT to merge multimodal medical images has been proposed.Experiments have been performed using two different sets of multimodal medical images. Theresults show that the proposed fusion method significantly outperforms the recent fusiontechniques reported in the literature.

Because of the limit of the optical lens’s depth, the objects of different distance usually cannot be at the same focus in the same picture, but multi-focus image fusion can obtain fusion image with all goals clear, improving the utilization rate of the image information ,which is helpful to further computer processing. According to the imaging characteristics of multi-focus image, a multi-focus image fusion algorithm based on redundant wavelet transform is proposed in this paper. For different frequency domain of redundant wavelet decomposition, the selection principle of high-frequency coefficients and low-frequency coefficients is respectively discussed .The fusion rule is that,the selection of low frequency coefficient is based on the local area energy, and the high frequency coefficient is based on local variance combining with matching threshold. As can be seen from the simulation results, the method given in the paper is a good way to retain more useful information from the source image , getting a fusion image with all goals clear.

Astrocytoma is a type of cancer that can form in the brain or spinal cord. It is begins in cells called astrocytes that support nerve cells. Astrocytoma signs and symptoms depend on the location of the tumor. In the analysis of such indicative patients, these tumors of brain can be visualized using a feature based fusion of input images. Multimodality image fusion has played an important role to diagnose the diseases for clinical treatment analysis and enhancing the performance and precision of the computer assisted system. In a recent development of medical field single multimodal medical image cannot provide all the details of human body. For example, the soft tissue information can be represented by magnetic resonance imaging, computed tomography imaging represent the bones dense structure with less distortion. In this paper, proposed method to merge the discrete fractional wavelet transform (DFRWT) with dual tree complex wavelet transform (DTCWT) based hybrid fusion technique for multimodality medical images. The developed fusion algorithm is experienced on the pilot study datasets of patients affected with astrocytoma disease. The fused image conveys the superior description of the information than the source images. Experimental results are evaluated by the number of well-known performance evaluation metrics.

Image compression or fusion is the concept of identifying in-depth parameters of disease variables, and requires output images that preserve all the viable and prominent information that is gathered from source images without any further introduction of artifacts or unnecessary distortions. Measurement of images for prospective evaluation and image fusion depends on various performance measures, such as structure similarity index, standard deviation, edge detection, correlation coefficient and high pass correlation, average gradient, root-mean-square error, peak signal-to-noise ratio, entropy, etc. This review discusses various medical image fusion modalities focused on Principal Component Analysis, Independent Component Analysis, and wavelet transform. An introduction to the usefulness of such modalities is presented, suggesting safe hybrid modality combinations that could greatly enhance the image fusion process. Novel trends in medical image fusion techniques to achieve a perfectly desired, quality image, the future prospects of an ideal technique for medical imaging, and recognition of diseases are covered.

Image fusion is a challenging research area which is useful in various image processing applications. Image fusion integrates information from multiple source images into a single composite image for better visual quality and information content than any of its source images. In the present paper, we have proposed a new median based image fusion algorithm using nonsubsampled shearlet transform. Nonsubsampled shearlet transform is a powerful multiscale geometrical analysis (MGA) tool having rich mathematical structure, high directionality, anisotropy and shift-invariance features. Due to these features nonsubsampled shearlet transform can efficiently capture information of the source images in its coefficient sets. The coefficient sets of the source images are fused by using a new median based fusion rule. Median is an important statistical measurement, which is enriched with two outstanding properties that are edge preserving and robustness against noise. Hence, median based fusion rule increases the quality of fused image. The proposed fusion rule is simple and easy to understand. Strength of the proposed fusion method is verified visually as well as quantitatively by comparing it with different state of the art methods. We have performed experiments on three different types of images (medical, remote sensing and multifocus). Results of the experiments confirm that the proposed method outperform in comparison with other state-of-the-art fusion methods visually as well as quantitatively in terms of different quantitative performance measures such as entropy, standard deviation, edge strength, fusion factor, and running time.

This paper focuses on the evaluation of noisy image fusion for medical images obtained from different modalities. In general, medical images suffer from poor contrast and are corrupted by blur and noise due to the imperfection of image capturing devices. In order to improve the visual and quantitative quality of the fused image, we compare two algorithms with other fusion techniques. The first algorithm is based on Dual Tree Complex Wavelet Transform (DTCWT) while the second is based on Scale Mixing Complex Wavelet Transform (SMCWT). The tested algorithms are using different fusion rules in each one, which leads to a perfect reconstruction of the output (fused image), this combination will create a new method which exploits the advantages of each method separately. DTCWT presents a good directionality since it considers the edge information in six directions and provide approximate shift invariant as well as SM-CWT, the goal of PCA is to extract the most significant features (wavelet coefficients in our case) to improve the spatial resolution. We compared the tested methods visually and quantitatively to recent fusion methods presented in the literature over several sets of medical images at multiple levels of noise. Further, the tested fusion algorithms have been tested up to the important level of Gaussian, salt & pepper and speckle noise (350 test). For the quantitative quality, we used several well-known fusion metrics. The results show that the tested methods outperform each method individually and other algorithms proposed in the literature.

In this paper, a gray multi-focus image fusion algorithm based on fuzzy theory classification and Dual-Tree Complex Wavelet Transform is proposed in accordance with fusion of gray multi-focus images. Fuzzy theory is introduced to remove uncertainty when choosing sub-band coefficients in smooth regions after Dual-Tree Complex Wavelet Transform. Fuzzy membership is used to measure the mapping relation between different feature types of images and corresponding pixels gray values. The uncertainty probably exists in fusion process is removed efficiently. Efficient fusion and detail enhancement of images are realized. Simulation results verify the effectiveness and superiority of proposed algorithms.

Microarray technology has become a power tool in the field of bioinformatics. It is used to measure gene expression levels and similar to any other image capturing processes is prone to noise. There are different kinds of noise, during preparation, hybridization and scanning in microarray images which usually are modeled by Gaussian noise. Since introduction of wavelets in 1970s, many more forms and extensions of this transform have been developed and used, such as stationary wavelet transform (SWT), complex wavelet transform (CWT), curvelet transform (CURV) and contourlet transform (CNT). By developing of more sparse transforms, it is important to have a perspective of how efficient the transforms are in different applications, such as microarray image analysis. In this paper, we compare the efficiency of common sparse transforms including ordinary discrete wavelet transform (DWT), SWT, CWT, CURV, CNT, Contourlet-SD decomposition, steerable pyramid (STP) and shearlet transform (SHR) for microarray image denoising. Therefore after converting microarray image into x-let transform, BayesShrink method, soft and hard thresholding are used to perform denoising of these images. Both local and general thresholds are calculated for each subband in order to evaluate the effect of incorporating intrascale dependency on top of sparsity property in statistical modeling of x-let's coefficients. Our simulation results show that CWT and SHR outperforms the others when using global thresholding and SWT is the preferred transform when using local thresholding. Although STP and SHR have better performance for some criteria like structural similarity (SSIM) index, but CWT is faster.

Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information. Firstly, this method uses two groups of large-scale and small-scale decomposition schemes that are structurally complementary, to perform two-scale double-layer singular value decomposition of the image separately and obtain low-frequency and high-frequency components. Then, the low-frequency components are fused by a rule that integrates image local energy with edge energy. The high-frequency components are fused by the parameter-adaptive pulse-coupled neural network model (PA-PCNN), and according to the feature information contained in each decomposition layer of the high-frequency components, different detailed features are selected as the external stimulus input of the PA-PCNN. Finally, according to the two-scale decomposition of the source image that is structure complementary, and the fusion of high and low frequency components, two initial decision maps with complementary information are obtained. By refining the initial decision graph, the final fusion decision map is obtained to complete the image fusion. In addition, the proposed method is compared with 10 state-of-the-art approaches to verify its effectiveness. The experimental results show that the proposed method can more accurately distinguish the focused and non-focused areas in the case of image pre-registration and unregistration, and the subjective and objective evaluation indicators are slightly better than those of the existing methods.

Photoacoustic (PA) imaging is capable of visualizing the three-dimensional structures of light-absorbing objects, such as blood vessels, and it is often integrated with ultrasound (US) imaging for comprehensive anatomical and functional imaging of biological tissues. For wide field-of-view imaging, volumetric PA or US data from multiple scans are merged to image large objects, such as the entire organ of humans or the entire body of small animals. This study investigated improvements in the volumetric data-merging process of wide-field PA/US imaging using the dual-tree complex wavelet transform (DT-CWT) technique. First, it was shown to effectively suppress boundary seam artifacts in the combined scan data, enabling smooth fusion while preserving critical features. Second, a noise-reduction imaging method based on this technique was applied to effectively enhance the image contrast and spatial resolution. Finally, we demonstrated the DT-CWT-based fusion of vascular images from PA and US volumes, which provided an integrated visualization of the peripheral vasculature. These results demonstrate that DT-CWT has the potential to improve wide-field PA/US images in terms of seamless fusion of multi-scan or multi-modal volume data, enhanced visualization of structures, noise reduction, and contrast improvement.

The importance of Satellite Aperture Radar (SAR) imagery systems is increasing day-by-day in various field such as earth observation, hi-technology war mechanisms, etc. The images captured by SAR imagery systems are mainly used to detect and classify objects captured in the images. Due to the complexity of the image capturing process, SAR images can be highly noisy – often consisting of multiplicative noise, also known as speckle. To detect or classify objects in SAR images, speckle noise must be removed from images. During despeckling process, the preservation of important information in the SAR images while removing noise such as edge or patterns is a crucial task. Despeckling methods are liable to compromise on edge preservation ability while aspiring for good quality denoised images. Many researchers have proposed wavelet transform based despeckling approaches such as Discrete Wavelet Transform (DWT), Undecimated Wavelet Transform (UDWT), Dual Tree Complex Wavelet Transform (DTCWT). In these approaches, finding the best values of the coefficients plays an important role in yielding excellent denoised images with preserved edges. In this paper, we have proposed a novel optimization framework that optimizes thresholding coefficients of DTCWT despeckling method for SAR images. The proposed optimization framework is based on Fruit Fly Optimization (FOA) algorithm. The approach is a multi-objective optimization algorithm that is used to find maximum values for Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM) and Equivalent number of look (ENL). The maximum value of MSSIM indicates high edge preservation capacity, whereas maximum PSNR value indicates good quality denoising of images. The maximum ENL value represents a good speckle-noise smoothing capability. We have applied our framework over some classical images as well as over SAR images of MSTAR dataset. In our experiments, we found that our proposed framework results in the excellent PSNR 36.87 dB, 35.4 dB and 37.8 dB, MSSIM values 0.92, 0.93 and 0.92, respectively, in the case of lena image, MSTAR dataset image and TerraSAR-X dataset image.

Due to the limited depth-of-focus of optical lenses, it is often difficult to get an image that contains all relevant objects in focus. Multi-focus image fusion algorithms can solve this problem effectively. Based on the analysis of the most widely used multi-focus image fusion algorithms, a new curvelet transform based multi-focus image fusion algorithm is proposed in this paper. According to the different frequency areas decomposed by Curvelet transform, the selection rules of the low frequency coefficients and the high frequency coefficients are discussed respectively. Low frequency coefficients and the high frequency coefficients are fused with NGMS (Neighborhood Gradient Maximum Selectivity) and LREMS (Local Region Energy Maximum Selectivity) separately in this paper. It is shown that the multi-focus image fusions by the proposed algorithm can obtain the same scene in focus image fusion, and have protruding advantage over other algorithms on subjective evaluation and objective evaluation.

Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

Image fusion helps to diagnose disorders such as tumours, cancer, bone fractures, ulcers, and stones in the body accurately. The fusion of complementary information from multiple images without losing information results in a single image with redundant information from multiple images. Fusing multi-modal medical images in clinical practice is a tedious but necessary task for accurate diagnosis and surgical planning. In the prevailing multi-modal fusion techniques, it is not possible to fully define the complex texture and edge identification of the lesion. In this paper, we introduce novel shift-invariant discrete wavelet transforms (DWTs) to deal with the issues of conventional multi-modal image fusion methods. In this paper, a pixel-level image fusion approach is proposed using the multi-resolution bi-orthogonal wavelet transform (BWT). We use the average and the gradient fusion rules to fuse wavelet coefficients at different levels of decomposition. In this paper, wavelet symmetry and linear phase, which are fundamental properties of BWT, were employed to reduce distortion in the fused image. This strategy maximizes fusion quality by minimizing information loss from individual images. This study compares the efficiency and accuracy of fusion methods using different parameters such as correlation coefficient (CC), entropy, peak signal-to-noise ratio (PSNR), and mutual information (MI).