Wavelet Fusion Research Articles

Iris based secure NROI multiple eye image watermarking for teleophthalmology

This paper presents a new secure multiple text and image watermarking scheme on cover eye image using fusion of discrete wavelet transforms (DWT) and singular value decomposition (SVD) for Teleophthalmology. Secure Hash Algorithm (SHA-512) is used for generating hash corresponding to iris part of the cover digital eye image and this unique hash parameter is used for enhancing the security feature of the proposed watermarking technique. Simultaneous embedding of four different watermarks (i.e. Signature, index, caption and reference watermark) in form of image and text using fusion of discrete wavelet transforms (DWT) and singular value decomposition (SVD) is achieved in this paper. The suggested technique initially divides the digital eye image into Region of interest (ROI) containing iris and Non-Region of interest (NROI) part where the text and image watermarks are embedded into the Non-Region of interest (NROI) part of the DWT cover image. The selection of DWT decomposition level for embedding the text and image watermarks depends on size, different characteristics and robustness requirements of medical watermark. The performance in terms of Normalized Correlation (NC) and bit error rate (BER) of the developed scheme is evaluated and analyzed against known signal processing attacks and `Checkmark' attacks. The method is found to be robust against all the considered attacks. The proposed multilevel watermarking method correctly extracts the embedded watermarks without error and is robust against the all considered attacks without significant degradation of the medical image quality of the watermarked image. Therefore the proposed method may find potential application in secure and compact medical data transmission for teleophthalmology applications.

Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area

In this study, we investigated the performance of different fusion and classification techniques for land cover mapping in Hilir Perak, Peninsula Malaysia using RADAR and Landsat-8 images in a predominantly agricultural area. The fusion methods used are Brovey Transform, Wavelet Transform, Ehlers and Layer Stacking and their results classified into seven different land cover classes which include (1) pixel-based classifiers (spectral angle mapper (SAM), maximum likelihood (ML), support vector machine (SVM)) and (2) Object-based (rule-based and standard nearest neighbour (NN)) classifiers. The result shows that pixel-based classification achieved maximum accuracy of the optical data classification using SVM in Landsat-8 with 74.96% accuracy compared to SAM and ML. For multisource data classification, the highest overall accuracy recorded for layer stacking (SVM) was 79.78%, Ehlers fusion (SVM) with 45.57%, Brovey fusion (SVM) with 63.70% and Wavelet fusion (SVM) 61.16%. And for object-based classifiers, the overall classification accuracy is 95.35% for rule-based and 76.33% for NN classifier, respectively. Based on the analysis of their performances, object-based and the rule-based classifiers produced the best classification accuracy from the fused images.

Improved hybrid algorithm for robust and imperceptible multiple watermarking using digital images

This paper presents a new robust hybrid multiple watermarking technique using fusion of discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and singular value decomposition (SVD) instead of applying DWT, DCT and SVD individually or combination of DWT-SVD / DCT-SVD. For identity authentication purposes, multiple watermarks are embedded into the same medical image / multimedia objects simultaneously, which provides extra level of security with acceptable performance in terms of robustness and imperceptibility. In the embedding process, the cover image is decomposed into first level discrete wavelet transforms where the A (approximation/lower frequency sub-band) is transformed by DCT and SVD. The watermark image is also transformed by DWT, DCT and SVD. The S vector of watermark information is embedded in the S component of the cover image. The watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DCT and inverse DWT. The watermark is extracted using an extraction algorithm. Furthermore, the text watermark is embedding at the second level of the D (diagonal sub-band) of the cover image. The security of the text watermark considered as EPR (Electronic Patient Record) data is enhanced by using encryption method before embedding into the cover. The results are obtained by varying the gain factor, size of the text watermark, and cover medical images. The method has been extensively tested and analyzed against known attacks and is found to be giving superior performance for robustness, capacity and reduced storage and bandwidth requirements compared to reported techniques suggested by other authors.

Multimodal biometric cryptosystem based on fusion of wavelet and curvelet features in robust security application

The authentication system used at the automated teller machines (ATM) is a unique personal identification number (PIN). This PIN can be easily tapped and misused. In this paper we propose a method in which the PIN is replaced by the biometrics of the individual to have a more secure transaction. A complete hardware system is designed to capture the biometric traits such as face, fingerprint and palm vein. The captured images are pre-processed and then features are extracted which are then fused at feature level. Cryptography is applied on the fused feature vector. Matching is performed using Euclidean distance at the server end. Palm vein is particularly chosen as a biometric trait along with widely used face and fingerprint because it is unique and is impossible to forge the vein pattern of an individual. Curvelet and Wavelet Transforms are used for the feature extraction. Experimental results indicate a good level of security and recognition rate of 91% and 89% is achieved on our own generated database. The results are promising when compared with other existing similar techniques.

Twofold processing for denoising ultrasound medical images.

Ultrasound medical (US) imaging non-invasively pictures inside of a human body for disease diagnostics. Speckle noise attacks ultrasound images degrading their visual quality. A twofold processing algorithm is proposed in this work to reduce this multiplicative speckle noise. First fold used block based thresholding, both hard (BHT) and soft (BST), on pixels in wavelet domain with 8, 16, 32 and 64 non-overlapping block sizes. This first fold process is a better denoising method for reducing speckle and also inducing object of interest blurring. The second fold process initiates to restore object boundaries and texture with adaptive wavelet fusion. The degraded object restoration in block thresholded US image is carried through wavelet coefficient fusion of object in original US mage and block thresholded US image. Fusion rules and wavelet decomposition levels are made adaptive for each block using gradient histograms with normalized differential mean (NDF) to introduce highest level of contrast between the denoised pixels and the object pixels in the resultant image. Thus the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST). The results indicate visual quality improvement to an interesting level with the proposed twofold processing, where the first fold removes noise and second fold restores object properties. Peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength (ES), image quality Index (IQI) and structural similarity index (SSIM), measure the quantitative quality of the twofold processing technique. Validation of the proposed method is done by comparing with anisotropic diffusion (AD), total variational filtering (TVF) and empirical mode decomposition (EMD) for enhancement of US images. The US images are provided by AMMA hospital radiology labs at Vijayawada, India.

Wavelet fusion for encrypting images with a few details

This paper introduces a new scheme for encrypting images with a few details based on wavelet fusion. In this scheme, the image with a few details to be encrypted is fused with another image that is rich in details utilizing the Discrete Wavelet Transform (DWT) prior to encryption. The fusion is a pre-processing step to change the homogeneity of flat areas in the images having a few details. RC6 or chaotic Baker map encryption are then performed on the fused image. Encryption with chaotic Baker map is just a permutation algorithm that cannot perform well on flat areas of the images, because the permutation yields approximately the same intensities. So, circular shifts on pixels are performed on the fused image prior to chaotic encryption to remove flat areas or reduce the degree of homogeneity. Chaotic encryption is then performed in the wavelet domain to increase the degree of diffusion. Several metrics are used in this paper for performance evaluation of the suggested ciphering schemes like visual inspection, histogram test, encryption quality analysis, and diffusion analysis. The robustness of the suggested image ciphering schemes is tested in the presence of noise before decryption. Simulation results demonstrated that the suggested image ciphering schemes provide a secure and effective way for encrypting images with few details.

Performance and analysis of high capacity Steganography of color images involving Wavelet Transform

This paper proposes a modified and simple method for high capacity Steganography in case of color images. This algorithm is usually based on the wavelet fusion technique which was not attempted for color images and it proves to be a simplest method than other techniques or algorithms for color images. Even though it uses transform domain technique which only provides robustness against attacks and eavesdropping and it is known that spatial domain techniques usually meet the capacity and quality requirements. In this paper the transform domain technique (Wavelet Transform) is used to attain high capacity along with security and maintains the quality of the cover image which acts as the key feature of this work. This improvement in the capacity–quality tradeoff is analyzed and proved experimentally in this paper and various image quality parameters have been evaluated to prove the above.

A new multi‐focus image fusion algorithm based on BEMD and improved local energy

A multi‐focus image fusion method, which is based on bidimensional empirical mode decomposition (BEMD) and improved local energy algorithm, is presented in this paper. First, the source image is decomposed by BEMD. Then maximum criterion combined with weighted average fusion rule based on local energy is applied to bidimensional intrinsic mode function components of the corresponding frequency segment. If the phase of the bidimensional intrinsic mode function coefficients decomposed by BEMD on two source images is the same, the local energy maximum criterion is used in the frequency coefficients of the fused image; else, if the corresponding phase is opposite, bidimensional intrinsic mode function coefficients of the fused image is determined by a weighted average method based on local energy. Finally, the fusion result is obtained by the inverse BEMD transformation on the fusion coefficient. Simulation shows that the proposed algorithm significantly outperforms traditional methods such as the maximum criterion, weighted average, and wavelet fusion rules. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Optical asymmetric watermarking using modified wavelet fusion and diffractive imaging

In most of the existing image encryption algorithms the generated keys are in the form of a noise like distribution with a uniform distributed histogram. However, the noise like distribution is an apparent sign indicating the presence of the keys. If the keys are to be transferred through some communication channels, then this may lead to a security problem. This is because; the noise like features may easily catch people׳s attention and bring more attacks. To address this problem it is required to transfer the keys to some other meaningful images to disguise the attackers. The watermarking schemes are complementary to image encryption schemes. In most of the iterative encryption schemes, support constraints play an important role of the keys in order to decrypt the meaningful data. In this article, we have transferred the support constraints which are generated by axial translation of CCD camera using amplitude-, and phase- truncation approach, into different meaningful images. This has been done by developing modified fusion technique in wavelet transform domain. The second issue is, in case, the meaningful images are caught by the attacker then how to solve the copyright protection. To resolve this issue, watermark detection plays a crucial role. For this purpose, it is necessary to recover the original image using the retrieved watermarks/support constraints. To address this issue, four asymmetric keys have been generated corresponding to each watermarked image to retrieve the watermarks. For decryption, an iterative phase retrieval algorithm is applied to extract the plain-texts from corresponding retrieved watermarks.

Multiscale Single Image Dehazing Based on Adaptive Wavelet Fusion

Removing the haze effects on images or videos is a challenging and meaningful task for image processing and computer vision applications. In this paper, we propose a multiscale fusion method to remove the haze from a single image. Based on the existing dark channel prior and optics theory, two atmospheric veils with different scales are first derived from the hazy image. Then, a novel and adaptive local similarity-based wavelet fusion method is proposed for preserving the significant scene depth property and avoiding blocky artifacts. Finally, the clear haze-free image is restored by solving the atmospheric scattering model. Experimental results demonstrate that the proposed method can yield comparative or even better results than several state-of-the-art methods by subjective and objective evaluations.

Denoising Ultrasound Medical Images with Selective Fusion in Wavelet Domain

Abstract Ultrasound medical imaging is undoubtedly an incontestable tool which provides the view of the internal organs of a body. The no ionizing radiation exposure property of the ultrasound medical images made it more fitting for fetus imaging. The only major snag ultrasound imaging encompasses is speckle noise that results from constructive and destructive interference thereby degrading the quality of the image. This paper submits a twofold technique to remove this multiplicative speckle noise and to bring a contrast between the object of the interest and the remaining image. First fold includes block based hard (BHT) and soft thresholding (BST) on pixels in wavelet domain where in which the original ultrasound image is divided into Non Overlapping blocks of sizes 8, 16, 32 and 64. The second fold includes restoration of the object boundaries and texture with adaptive wavelet fusion which are lost by the blurring effect caused as a result of the first fold. Fusion of wavelet coefficients of original US image and block thresholded US images assuaged to restore the degraded object. Fusion rule and wavelet decomposition level are made adaptive for each block using gradient histograms with normalized differential mean (NDF) to introduce highest level of contrast between the denoised pixels and the object pixels in the resultant image. Thus the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST). Visual quality through twofold processing has improved to an interesting level. Peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength (ES), image quality Index (IQI) and structural similarity index (SSIM), measure the quantitative quality of the twofold processing technique. Validation of the proposed method is done by comparing with anisotropic diffusion (AD), total variational filtering (TVF) and empirical mode decomposition (EMD) for enhancement of US images. The US images are provided by AMMA hospital radiology labs at Vijayawada, India.

Soil Moisture Inversion Based on AMSR-E and MODIS Data Fusion: A Case Study of Huaihe River Basin

In this paper, the correlation between fused data and original data, the measured soil and the precipitation data over Huaihe river basin by exploring the inversion of soil moisture from the time and space based on the method of multi-source remote sensing data fusion has been studied. In order to fuse the AMSR-E data which is all-day and all-weather and can penetrate the earth surface to some extent, with the MODIS data that can reflect the surface condition and temperature characteristics, the method of wavelet fusion was carried out in MATLAB. The conclusions of this study are listed as follows: (1) the inversion result of the fused data based on AMSE-E and MODIS is much better than a single remote sensing data inversion; (2) the fused data based on AMSE-E and MODIS is sensitive to soil moisture change trend when the seasons alternated every year, especially in the spring, summer and autumn.

Variational model-based very high spatial resolution remote sensing image fusion

A remote sensing image fusion technique provides a mechanism for integrating multiple remotely sensed images to form an innovative image by using a certain algorithm for improving the spatial quality of the source image with minimal spectral distortion. Many algorithms, known as pan-sharpening algorithms, have been developed to improve the spatial resolution of multispectral (MS) images with a panchromatic (Pan) image. In the standard fusion methods, high spectral quality implies low spatial quality and vice versa. The utility of one Pan-sharpening model based on the variational model (VM) that consists of several energy terms is tested on very high spatial resolution images. In this model, the geometric structure matching term is used to inject the geometric structure of the Pan image, and the spectral matching term is utilized for preserving the spectral information. To balance the tradeoff between injecting the spatial information and preserving the spectral information, a static and a dynamic weight paradigm were introduced in this paper to control their relative contributions (static weights VM and dynamic weights VM). The evaluation of the experimental results on the QuickBird and WorldView-2 datasets shows that VM-based fusion models are better than the principal component analysis, Brovey transform fusion model, and Wavelet fusion model, and the dynamic weights VM performs better than the static weights VM. VM-based fusion models could be good options for very high spatial resolution remote sensing image fusion.

Poster — Thur Eve — 09: Evaluation of electrical impedance and computed tomography fusion algorithms using an anthropomorphic phantom

Integration of biological conductivity information provided by Electrical Impedance Tomography (EIT) with anatomical information provided by Computed Tomography (CT) imaging could improve the ability to characterize tissues in clinical applications. In this paper, we report results of our study which compared the fusion of EIT with CT using three different image fusion algorithms, namely: weighted averaging, wavelet fusion, and ROI indexing. The ROI indexing method of fusion involves segmenting the regions of interest from the CT image and replacing the pixels with the pixels of the EIT image. The three algorithms were applied to a CT and EIT image of an anthropomorphic phantom, constructed out of five acrylic contrast targets with varying diameter embedded in a base of gelatin bolus. The imaging performance was assessed using Detectability and Structural Similarity Index Measure (SSIM). Wavelet fusion and ROI‐indexing resulted in lower Detectability (by 35% and 47%, respectively) yet higher SSIM (by 66% and 73%, respectively) than weighted averaging. Our results suggest that wavelet fusion and ROI‐indexing yielded more consistent and optimal fusion performance than weighted averaging.

English

Usually the images taken by digital cameras are different from the original true color image in terms of color and brightness of the scene captured. Various factors like non uniform illumination, color of ambient light, low or high contrast, and over exposure or under exposure of some specific regions may leads to poor contrast which is annoying to human visual system. Thus, enhancement methods are required for color images. In this paper An image fusion is used to improve the performance of DC coefficient scaling method in the compressed DCT domain for enhancing the contrast of colour images. Using the multi resolution wavelet fusion preserves the brightness of the enhanced image. This improves the entropy of standard DCT based DC coefficient scaling method. For comparing the performance along with SNR and the absolute standard deviation difference is also used as parameters. The methods are tested on various true colour images from different environment

Stabilizing Atmospheric Turbulence in a Long Range Surveillance using Dark Channel Prior

Various types of turbulences affect the visual quality during video surveillance. Turbulence such as fog or haze is responsible for image contrast. Removing this turbulence is a challenging problem in video surveillance. We used a method for stabilizing this atmospheric turbulence using dual tree complex wavelet fusion. Informative ROIs are selected using frame selection method from good quality frames. Morphological erosion is done afterwards. Image fusion is done using dual tree complex wavelet transform so as to fuse two images. In this paper, dark channel prior mechanism is proposed for haze removal. Finally image quality assessment is done. This proposed method is shown to outperform existing one and provide enhance visual quality in the range of surveillance scenarios. General Terms Dual Tree Complex Wavelet Transform (DT-CWT).

Discrete wavelet transform based image fusion and de-noising in FPGA

Image fusion is an extensively discussed topic for improving the information content of images. The main objective of image fusion algorithm is to combine information from multiple images of a scene. The result of image fusion is a new image which is more feasible for human and machine perception for further image processing operations such as segmentation, feature extraction and object recognition. This paper explores the possibility of using the specialized wavelet approach in image fusion and de-noising. These algorithms are compared on digital microscope images. The approach uses an affine transform based image registration followed by wavelet fusion. Then the least squares support vector machine based frequency band selection for image denoising can be incorporated to reduce the artifacts. The indentations are to maximize resolution, decrease artifacts and blurring in the final super image. To accelerate the entire operations, it is proposed to offload the image processing algorithms to a hardware platform thereby the performance can be improved. FPGAs provide an excellent platform in implementing real time image processing applications, since inherent parallelism of the architecture can be exploited explicitly. Image processing tasks executed on FPGAs can be up to 2 orders of magnitude faster than the equivalent application on a general purpose computer.

Study on Adaptive and Fuzzy Weighted Image Fusion Based on Wavelet Transform in Trinocular Vision of Picking Robot

In order to improve the adaptive recognition abilities of picking robot in complex environment, a fusion approach of trinocular vision in wavelet domain based on fuzzy reasoning weight was proposed. Firstly, membership functions of fusion rules are determined by fuzzy reasoning of picking environmental features, and membership values of fusion types are calculated according to regional energy and match degree of origin images. Based on the maximum membership degree principle, fuzzy decision is carried on to determine the fusion types and fusion weight. Secondly, the mean weighted method and regional energy feature method are adopted respectively to carry on the low frequency as well as high frequency coefficients fusion among multi-source images by using two-level 2D wavelet, and the final fusion images are attained by inverse HIS transform based on inverse wavelet transform. Four groups of experiment show that in the complex picking environment like weak illumination and strong noise, the information entropy and average gradient of fused image that obtained by using the wavelet fusion method based on fuzzy reasoning weight are higher than that of traditional mean method, pyramid algorithm and wavelet packet method, which means that the fusion effect has been improved greatly.

FUSION OF WAVELET AND CURVELET COEFFICIENTS FOR GRAY TEXTURE CLASSIFICATION

This study presents a framework for gray texture classification based on the fusion of wavelet and curvelet features. The two main frequency domain transformations Discrete Wavelet Transform (DWT) and Discrete Curvelet Transform (DCT) are analyzed. The features are extracted from the DWT and DCT decomposed image separately and their performance is evaluated independently. Then feature fusion technique is applied to increase the classification accuracy of the proposed approach. Brodatz texture images are used for this study. The results show that, only two texture images D105 and D106 are misclassified by the fusion approach and 99.74% classification accuracy is obtained.

Stabilizing Atmospheric Turbulence in a Video Surveillance using Dual Tree Complex Wavelet Fusion

Many valuable data in a video can be lost because of atmospheric distortion such as mirage, haze, fog, etc. This is a challenge in video surveillance system. We propose complex wavelet fusion for atmospheric turbulance algorithm for this purpose. This algorithm consists of series of steps to extract the information from the video. Frame selection method is used for selection of informative ROI only from good quality frame. These ROI were then fused to get the required objects. The fusion is performed in the Dual Tree Complex Wavelet Transform (DT-CWT) which employs two different real discrete wavelet transforms (DWT) to provide the real and imaginary parts of the CWT. Finally Haze removal is done by using dark channel prior mechanism. The proposed method is shown to significantly enhance the visual quality in a range of video surveillance.