Homomorphic Transformation Research Articles

Speckle Noise Reduction in Ultrasound Images

Abstract: Ultrasound Imaging is used to examine various organs in the Human Body. However, in the process of obtaining a Ultrasound Image, Speckle noise is generated due to backscattered echo signals. So, a noise reduction method is required. To improve the speckle noise reduction, we propose a novel algorithm using characteristics of speckle noise and filtering methods based on Speckle Reducing Anisotropic Diffusion (SRAD) filtering, Discrete Wavelet Transform (DWT), Weighted Guided Image Filtering (WGIF) and Gradient Domain Guided Image Filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency subband images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform

SUPPRESSION OF SPECKLE NOISE IN MEDICAL IMAGES VIA SEGMENTATION-GROUPING OF 3D OBJECTS USING SPARSE CONTOURLET REPRESENTATION

Novel filtering method in medical images (MRI and US) that are contaminated by noise consisting of mixture speckle and additive noise is designed in this paper. Proposed method consists of several stages: segmentation of image areas, grouping of similar 2D structures in accordance mutual information (MI) measure, homomorphic transformation, 3D filtering approach based on sparse representation in contourlet (CLT) space with posterior filtering in accordance with MI weights similar 2D structures, and final inverse homomorphic transformation. During numerous experiments, the developed method has confirmed their superiority in term of visual image quality via human visual perception as well as in better criteria values, such as PSNR, SSIM, EPI and alfa for different test MRI and US mages corrupted by speckle noise.

COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network

COVID-19 had caused the whole world to come to a standstill. The current detection methods are time consuming as well as costly. Using Chest X-rays (CXRs) is a solution to this problem, however, manual examination of CXRs is a cumbersome and difficult process needing specialization in the domain. Most of existing methods used for this application involve the usage of pretrained models such as VGG19, ResNet, DenseNet, Xception, and EfficeintNet which were trained on RGB image datasets. X-rays are fundamentally single channel images, hence using RGB trained model is not appropriate since it increases the operations by involving three channels instead of one. A way of using pretrained model for grayscale images is by replicating the one channel image data to three channel which introduces redundancy and another way is by altering the input layer of pretrained model to take in one channel image data, which comprises the weights in the forward layers that were trained on three channel images which weakens the use of pre-trained weights in a transfer learning approach. A novel approach for identification of COVID-19 using CXRs, Contrast Limited Adaptive Histogram Equalization (CLAHE) along with Homomorphic Transformation Filter which is used to process the pixel data in images and extract features from the CXRs is suggested in this paper. These processed images are then provided as input to a VGG inspired deep Convolutional Neural Network (CNN) model which takes one channel image data as input (grayscale images) to categorize CXRs into three class labels, namely, No-Findings, COVID-19, and Pneumonia. Evaluation of the suggested model is done with the help of two publicly available datasets; one to obtain COVID-19 and No-Finding images and the other to obtain Pneumonia CXRs. The dataset comprises 6750 images in total; 2250 images for each class. Results obtained show that the model has achieved 96.56% for multi-class classification and 98.06% accuracy for binary classification using 5-fold stratified cross validation (CV) method. This result is competitive and up to the mark when compared with the performance shown by existing approaches for COVID-19 classification.

Research on intellectual property protection, innovation, and data protection algorithm based on wireless communication network

In today's world, with the developing technologies, data protection has become an absolute requirement, many organizations are focusing on the security of the data and information. Because many unauthorized individuals can hack computer system, putting the confidential files of the organization at danger. Therefore, there is need to employ various data security techniques in order to secure information. Furthermore, to resolve the problem of high energy consumption caused by large amount of data transmission in data protection algorithm, a data protection algorithm based on wireless communication network is proposed. Furthermore, we investigated that nowadays the protection of intellectual property is become significant issue. Because intellectual property security is essential for promoting innovation. Without intellectual property protection, individuals and firms would not gain the full advantages of their creations and would devote less time to development. The communication channel and data transmission link are established. Each node calculates the residual energy when clustering and sends it to the cluster head before the link is generated. The random number of nodes is encrypted and sorted according to the order of data link. Based on the wireless communication network, the privacy data within and between clusters are fused, and the data difference of each node is calculated respectively. Each cluster head uses homomorphic transformation to realize data sharing. The data protection algorithm is designed. The node data is partitioned and encrypted and decrypted by hybrid operation. The fusion results are uploaded to the base station to complete data protection. Apart from this, in this research, we proposed various methodologies in order to secure information as much as possible including text, photography, forms, videos and many more. Experimental results show that compared with CPDA algorithm, CMT algorithm and HRCDA algorithm, the data protection algorithm designed in this paper consumes the least total energy in each round. The algorithm can protect the privacy of data, reduce the consumption of data, and extend the life cycle of the whole network.

Novel criteria for global robust stability of dynamical neural networks with multiple time delays

This research article considers the problem regarding global robust asymptotic stability of the general type of dynamical neural networks involving multiple constant time delays. Some new sufficient criteria are proposed for the existence, uniqueness and global asymptotic stability of the equilibrium point of this neural network model whose network parameters possess uncertainties. This paper will first address the existence and uniqueness problem for equilibrium points by utilizing the Homomorphic transformation theory. Secondly, by exploiting a novel Lyapunov functional candidate, the sufficient conditions for asymptotic stability of equilibrium points of this class of delayed neural networks will be established. The derived robust stability conditions are expressed independently of the constant time delay parameters and define some novel relationships among network parameters of the considered neural network. Thus, the applicability and validity of the obtained robust stability conditions for delayed-type neural networks can be easily tested. The comprehensive comparisons among the results of the current article and some of previously derived corresponding results will also be made by giving an illustrative numerical example.

Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Synthetic aperture radar (SAR) images are inherently degraded by the speckle noise due to the coherent imaging, which may affect the performance of subsequent image analysis task. To address this problem, a weighted sparse representation-based method is proposed in this article for SAR image despeckling. The homomorphic transformation is first adopted to convert multiplicative noise into additive one. Second, similar patches are grouped together to learn the adaptive dictionaries and sparse coefficients based on nonlocal self-similarity constraint. Moreover, weighted regularizations are adopted for coefficients to boost the performance. Finally, despeckling images are obtained via exponential transformation. Experimental results on synthetic and real-world SAR images demonstrate that our proposed method outperforms several state-of-the-art methods in terms of both quantitative measurements and visual quality.

A Novel Dual Image Watermarking Technique Using Homomorphic Transform and DWT

Abstract In this paper a new technique of dual image watermarking is proposed for protection of ownership rights which utilizes salient properties of homomorphic transform (HT), discrete wavelet transform (DWT), singular value decomposition (SVD) and Arnold transform (AT). In embedding algorithm host image is splitted into reflectance and illumination components using HT, DWT is further applied to the reflectance component resulting in frequency subbands (HL and LH) which are transformed by SVD. Two image watermarks are selected for embedding process whereas security of proposed algorithm is strengthen by performing scrambling of second watermark through AT. Both watermarks are transformed with DWT and SVD. Singular values (SVs) of both transformed watermark are embedded into SVs of host image. Simulation results clearly signifies for high robustness and imperceptibility of proposed algorithm as it is examined under various attacks. Superiority of proposed technique is illustrated by comparing it with other reported methods.

A reliable and secure image watermarking algorithm using homomorphic transform in DWT domain

Image watermarking is one of the reliable methods which easily retains integrity and confidentiality of images against illegitimate users thereby ensuring copyright protection. In this paper a new image watermarking technique is proposed which employs fusion of discrete wavelet transform (DWT), homomorphic transform (HT), singular value decomposition along with Arnold transform (AT). In this work first level DWT is performed on host image which results in various subbands (LL, HL, LH and HH). HL subband is chosen which is further transformed by HT to decompose it in illumination and reflectance components. The watermark is scrambled with AT in order to strengthen security towards malicious attacks. Scrambled watermark is now embedded into singular values of the reflectance component. As reflectance component contains prominent image features, hence embedding in this part provides better immunity against several attacks and imperceptibility is also attained as this component varies very rapidly. Thus, major aim for proposed watermarking technique is achieved. The proposed scheme is implemented with numerous wavelet families such as Haar, Coiflet, symlet and biorthogonal. The main idea for implementing with different wavelets is to judge which wavelet demonstrates good robustness against various attacks. Proposed scheme performance is also evaluated by examining comprehensively under several attacks such as rotation, sharpening. Results clearly prove high robustness for the proposed scheme. Superiority of proposed scheme is clearly indicated by comparing it with other commonly—used schemes as reported by other researchers. Benchmark attacks like “Checkmark” attacks are also verified for the proposed scheme.

Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.

A Secured and Robust Medical Image Watermarking Approach for Protecting Integrity of Medical Images

AbstractIn this paper, a novel medical image watermarking (MIW) technique for tele ‐ medicine applications is proposed. In this approach homomorphic transform (HT), redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) transforms are successfully utilized for developing an effective watermarking technique. Reflectance component of medical host image is obtained using HT, on which RDWT decomposition and SVD are applied. Similarly medical watermark image is processed with RDWT and SVD. Singular values (Svs) were used for embedding the watermark image into host image. 2‐D chaotic Arnold transform (AT) is used for providing extra security to the watermark image by encrypting the watermarked image. Moreover, proposed technique is also tested for several wavelet families. Robustness and imperceptibility of the proposed technique is enhanced as depicted in experimental results under various attacks. Furthermore, better robustness is attained from proposed technique on comparing it with other formerly reported schemes.

Secure and Robust Image Watermarking Scheme Using Homomorphic Transform, SVD and Arnold Transform in RDWT Domain

The main objective for a watermarking technique is to attain imperceptibility, robustness and security against various malicious attacks applied by illicit users. To fulfil these basic requirements for a scheme is a big issue of concern. So, in this paper, a new image watermarking method is proposed which utilizes properties of homomorphic transform, Redundant Discrete Wavelet Transform (RDWT), Arnold Transform (AT) along with Singular Value Decomposition (SVD) to attain these required properties. RDWT is performed on host image to achieve LL subband. This LL subband image is further decomposed into illumination and reflectance components by homomorphic transform. In order to strengthen security of proposed scheme, AT is used to scramble watermark. This scrambled watermark is embedded with Singular Values (SVs) of reflectance component which are obtained by applying SVD to it. Since reflectance component contains important features of image, therefore, embedding of watermark in this part provides excellent imperceptibility. Proposed scheme is comprehensively examined against different attacks like scaling, shearing etc. for its robustness. Comparative study with other prevailing algorithms clearly reveals superiority of proposed scheme in terms of robustness and imperceptibility.

One-to-many image encryption with privacy-preserving homomorphic outsourced decryption based on compressed sensing

As a classical framework for signal compression and reconstruction, compressed sensing (CS) has dramatically improved the state-of-the-art in many fields such as image processing, wireless communication, medical imaging and optics. Since CS integrates data compression and encryption, CS-based cryptographic primitives have attracted great attention in recent years. Note that almost all the existing CS-based encryption schemes are one-to-one symmetric cryptosystems. Unlike traditional permutation-diffusion based symmetric cryptosystems (e.g., DES and AES), CS-based encryption schemes do not have the characteristic of high efficiency because decryption procedures require to perform complex and time-consuming reconstruction algorithms. In this paper, we break the traditional one-to-one encryption framework of CS-based cryptosystems, and present an efficient one-to-many encryption model for flexible application scenarios. Specifically, the information sender first encrypts several plaintext images simultaneously for different users according to our proposed attribute-based key distribution mechanism, and then sends the ciphertext to the cloud storage server. Any user who has one of attributes can efficiently obtain the corresponding plaintext by using an outsourced decryption server (ODS). To prevent the ODS from stealing the plaintexts for illegal activities, we employ a homomorphic transformation algorithm which can transform the original reconstruction problem into a variant one. The experimental results and the comparison with similar works demonstrate the feasibility and the superiority of the proposed scheme.

Homomorphic Two-Stage Image Sequence Filtering Algorithm in the Presence of Correlated Speckle Noise

Introduction. An images received from synthetic-aperture radar and ultrasonic medical coherent systems are distorted by speckle noise which can be spatially correlated. Sequence frame processing is applied due to poor quality of source images. The large number of filters for processing the images distorted by a multiplicative speckle noise were developed at the present time. Most of existing filters don’t take into account spatial correlation in the speckle noise which leads to lower efficiency of noise suppression. Image sequence processing leads to processed data volume increase. Thus, a problem of computing complexity and practical implementability of the filtering algorithms used for speckle noise suppression is even more relevant. Theoretical results. The task of filtering the sequence of images in the presence of correlated speckle noise becomes the task of filtering a signal component in the presence of the additive correlated noise as a result of homomorphic transformation. An expression for posteriori probability density of signal component pixel and the correlated noise pixel in each point of the sequence of images was created using property of the conditional independence. It’s used for combining received posterior distributions at the first stage as a result of executing one-dimensional non-linear filtering. The algorithm of computation of mathematical expectation and correlation matrix of joint posteriori probability density of a signal and correlated noise pixels in each point for a case of gaussian posterior distributions for non-causal intraframe and causal interframe processing is synthesized. Experimental results. The developed algorithm allows to receive a gain in signal-to-noise ratio in 5.8-1 dB for a model example by processing from one to five frames compared to interframe averaging algorithm. The number of segmentation errors for the images filtered by Otsu algorithm using three frames is 2 % for the developed algorithm and 18 % for interframe averaging. Conclusion. The homomorphic two-stage filtering algorithm of the sequence of images in the presence of correlated speckle noise was developed. It uses two-stage joint filtering of a signal component and the additive correlated noise with intraframe not causal and interframe causal processing after homomorphic transformation.

(Non-) homomorphic approaches to denoise intensity SAR images with non-local means and stochastic distances

The development of new methods to denoise images still attract researchers, who seek to combat the noise with the minimal loss of resolution and details, like edges and fine structures. Many algorithms have the goal to remove additive white Gaussian noise (AWGN). However, it is not the only type of noise which interferes in the analysis and interpretation of images. Therefore, it is extremely important to expand the filters capacity to different noise models present in li-terature, for example the multiplicative noise called speckle that is present in synthetic aperture radar (SAR) images. The state-of-the-art algorithms in remote sensing area work with similarity between patches. This paper aims to develop two approaches using the non local means (NLM), developed for AWGN. In our research, we expanded its capacity for intensity SAR ima-ges speckle. The first approach is grounded on the use of stochastic distances based on the G0 distribution without transforming the data to the logarithm domain, like homomorphic transformation. It takes into account the speckle and backscatter to estimate the parameters necessary to compute the stochastic distances on NLM. The second method uses a priori NLM denoising with a homomorphic transformation and applies the inverse Gamma distribution to estimate the parameters that were used into NLM with stochastic distances. The latter method also presents a new alternative to compute the parameters for the G0 distribution. Finally, this work compares and analyzes the synthetic and real results of the proposed methods with some recent filters of the literature.

Classification of Breast Masses in Mammograms Using 2D Homomorphic Transform Features and Supervised Classifiers

Classification of Breast Masses in Mammograms Using 2D Homomorphic Transform Features and Supervised Classifiers

Unbiased-average minimum biased diffusion speckle denoising approach for synthetic aperture radar images

Means of synthetic aperture radar (SAR) images represent the radiation densities of scenes, and the preservation of means is significant in speckle denoising for the application of SAR images. We provide an improved scheme of the minimum biased diffusion (MinBAD) algorithm for speckle denoising using partial differential equations. Considering the characteristics of SAR speckle and the radiation accuracy for postprocessing needs, several improvements such as normalization, homomorphic transformation, and average-preserving processing are introduced into the MinBAD algorithm. Besides the equivalent number of looks and edge preserving index, a new index, radiation accuracy error, is defined to evaluate the denoising effect. Experimental results for both artificial images and real SAR images are used to validate the performance of the proposed unbiased-average MinBAD speckle reducing approach.

Development of a Robust Photogrammetric Metrology System for Monitoring the Healing of Bedsores

AbstractA three‐camera close range photogrammetric system for robust and precise measurement of bedsores has been designed and constructed. MEDPHOS (MEDical PHOtogrammetric System) consists of three synchronised cameras with convergent optical axes. A light projector is fixed in the centre of the rig that holds the cameras. A special dot pattern is projected onto the surface to be measured, to compensate for the lack of natural texture on the wound surface. The proposed algorithm consists of the following steps: the cameras and projector are calibrated so that all interior and exterior parameters are known; tailored image segmentation procedures are developed and applied for the detection of the projected pattern dots from the uneven background of the images using morphologic operators; and watershed transformation is used to tackle the problem of overlapping pattern dots. To reduce the effects of non‐uniform illumination and specular reflection of light due to humidity (often the case with wounds), a homomorphic transformation is developed and applied to the images. After segmentation of the images, a connected‐component labelling procedure is used to establish the points for matching. The centroids of these components were precisely calculated. Intensity‐based image matching has been tested without yielding satisfactory results due to the significant deviation from the Lambertian reflection assumption used for solving the correspondence problem. This problem is reliably solved by developing a new algorithm based on geometric constraints that allow feature‐based matching and do not need approximate values of the location of the targets in the images. This robust three‐focal constraint is found to be very effective for matching provided the necessary conditions for the system configuration are met. Auxiliary photometric constraints together with the calibrated projector (which is treated like an active camera) also serve as additional sources of information for reducing the number of remaining ambiguities and checking the consistency of the results. Almost all of the required biometric information can be obtained rapidly, robustly and easily using MEDPHOS. Experimental results showed the effectiveness of the proposed technique.

Signal-dependent film grain noise generation using homomorphic adaptive filtering

A technique for the generation of signal-dependent film grain noise is presented. It finds applications in television and motion picture productions where artificial film grain noise is added to computer-generated synthetic images to give them a realistic appearance. After decoupling the signal from the noise, using the generalised homomorphic transformation, the noise is removed from the noisy image using the proposed adaptive filtering scheme. The noise parameter is then estimated using the higher-order statistics (skewness or kurtosis) of the observed image and the filtered image statistics, and it is used to generate film grain noise. Computer simulation results demonstrate the effectiveness of the proposed method.

Formal verification of digital systems by automatic reduction of data paths

Verification of properties (tasks) on a system P containing data paths may require too many resources (memory space and/or computation time) because such systems have very large and deep state spaces. As pointed out by Kurshan, what is needed is a reduced system P' which behaves exactly as P with respect to the properties that must be proved, but more compact than P, so that the verification can be easily performed. The process of finding P' from P is called reduction. P is specified by a network of interacting finite-state machines for data paths and controllers, and tasks are specified by finite-state automate. The verification of a task T on P is performed by the language containment check L(P)/spl sube/L(T), where L(P) is the language generated by P and L(T) is the language accepted by T. It has been shown that, under appropriate conditions, the system P can be reduced to P' and the task T to T' such that L(P')/spl sube/L(T')/spl hArr/L(P)/spl sube/L(T). The direct language containment check L(P)/spl sube/L(T) is no longer needed; it is replaced by L(P')/spl sube/L(T'), which is less expensive. More specifically, for the purpose of simplifying the verification of some properties, the system implementation is abstracted locally with respect to the behavior under observation (i.e., bottom-up reduction), in the context of an integrated top-down design/verification technique. The tasks that one may want to verify can express both safety and fairness constraints. In this paper, we prove that the reduction of some data paths to four-state, nondeterministic finite-state machines, and the redundancy removal performed on the controllers is a homomorphic transformation, so that the simplified language containment check can automatically be applied without testing the validity of the homomorphism. This homomorphism correctness verification, required when a formal proof is not available, can be executed using a tool like Cospan, but it may not be completed when the state space to be traversed is too large and deep. The redundancy removal performed on the controllers is important because it eliminates the spurious behaviors introduced in the system by the nondeterminism of the reduced data paths. Redundancy, in fact, may induce a failure in the verification of L(P')/spl sube/L(T'), while L(P)/spl sube/L(T) actually holds. In order to show the effectiveness of the proposed methodology, we verify properties on an extended version of the Mead-Conway Traffic Light Controller, on a modified IRQ communication protocol, and on a relatively prime integers checker and generator.

Iterative homomorphic technique for speckle reduction in synthetic-aperture radar imaging

The presence of speckle in radar images reduces the radiometric resolution and renders less efficient the procedures for texture class discrimination. We present an algorithm devoted to speckle reduction in syntheticaperture radar images based on a homomorphic filter coupled with a Wiener filter. To construct the Wiener filter we analytically evaluate the autocorrelation function of the noise, starting from the first two orders of statistics of the noise, before performing the homomorphic transformation (a logarithmic one, in the case of multiplicative noise), and the autocorrelation of the noise-free image is evaluated by an iterative procedure. The algorithm, tested on both simulated and actual synthetic-aperture radar images, provides very promising results and shows the usefulness of the proposed method.