Hybrid Discrete Wavelet Transform Research Articles

Analysis and effectiveness of deeper levels of SVD on performance of hybrid DWT and SVD watermarking

In this paper, an analysis on hybrid Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for image watermarking is carried out to investigate the effect of a deeper level of the SVD on imperceptibility and robustness to resist common signal processing and geometric attacks. For this purpose, we have designed two hybrid watermarking schemes, the first one with DWT and first level of SVD, whereas, in the second scheme, the same design is employed with a second level of SVD. In this experiment, a comprehensive analysis is performed on the two designed schemes and the effect of robustness and imperceptibility is compared in the first and second levels of SVD in each DWT sub-band. Having analyzed more than 100 medical and non-medical images in standard datasets and real medical samples of patients, the experimental outcomes show a remarkable increase in both imperceptibility and robustness in the second level of SVD, in comparison to the first level. In addition, the achieved result shows that the SVD2 scheme offers the highest imperceptibility in the LL sub-band (more than 60 dB on average PSNR), with satisfactory robustness against noise attacks, but less persistence in some geometric attacks such as cropping. For the HH sub-band, strong robustness against all types of tested of attacks is obtained, though its imperceptibility is slightly lower than the achieved PSNR in the LL sub-band. In HH sub-band, an average growth of 5 dB in PSNR and 2% in NC can be observed from the second level of SVD in comparison to the first level. These results make SVD2 a good candidate for content protection, especially for medical images.

A Transfer learning model for high secure image steganography using ResNet-50

Cryptography is commonly employed in data security, but one of the main disadvantages of the scheme would be that it attracts attention to just the perpetrator, which results throughout the exposure of the data. In order to solve these issues, digital image steganography is among the several techniques used to provide protection during data transmission. Capacity embedding and distortion are indeed the two key factors that play a key role when hiding a message in image. Therefore, this paper explores strategies consisting of two steps leading to the creation of that very stable framework with a large embedding ability. The first step determines an arbitrary image by morphing through hybrid discrete wavelet transform (HDWT) in order to produce a cover image that relies on the characteristics of both the chosen image. That second step integrates a text message of adequate duration using the transfer learning based ResNet50 architecture, integrating multi-keys and compound operators into the algorithm to provide robust protection. Simulation results shows that the proposed method resulted in superior PSNR and SSIM performance as compared to state of art approaches.Finally, the robustness and imperceptibility of the proposed method is improved, which reduced the attacks on the system.

A Risk-Free Discount Rate Prediction Model for Mineral Project Evaluation Using a Hybrid Discrete Wavelet Transform and Artificial Neural Network

The discount rate input parameter of Net Present Value (NPV) in mineral project evaluation is a function of a risk-free rate and risk premium component. To obtain a reliable NPV, it is important to estimate each of these components. This study employs a hybrid approach to predict risk-free rate using Discrete Wavelet Transform and Artificial Neural Network (DWT-ANN). The DWT-ANN model was tested using London Interbank Offered Rate (LIBOR) dataset from 1986 to 2020. The results showed that Discrete Wavelet Transform-Radial Basis Function Neural Network (DWT-RBFNN) of the three different hybrid algorithms developed and applied performed best in predicting the risk-free rate. This is because it achieved the lowest root mean square error of 0.0376 and the highest correlation coefficient of 0.9995. The DWT-RBFNN model can be a useful alternative tool for predicting risk-free rate, which is a key input parameter for the determination of discount rate.

Noise estimation in 2D MRI using DWT coefficients and optimized neural network

Robust estimation of noise strength in Magnetic Resonance Images (MRIs) is a task of great importance due to its extensive applications in image post-processing techniques. Many noise estimation algorithms have been proposed in the past to retrieve noise characteristics of the magnitude image. These algorithms rely on either the spatial or transform domain information of the image. In this research article, we propose a noise estimation algorithm that utilizes a hybrid Discrete Wavelet Transform (DWT) and edge information suppression based algorithm to estimate the strength of noise in magnitude MR images. The wavelet coefficients corresponding to spatial domain edges are eliminated using a down-sampled complement of the Sobel edge map. The distribution of average noise energy in spatial and transform domain which follows Parseval’s theorem is utilized for calculating the initial noise estimate. Further, the robustness of the proposed algorithm is enhanced using Feedforward Neural Network. The proposed algorithm is observed to be computationally fast and accurate. Results on synthetic and clinical T1- and T2-w brain MRIs following Gaussian or Rician distribution show better performance than the existing benchmark algorithms over a wide range of input noise.

Long-term trend analysis of rainfall using hybrid Discrete Wavelet Transform (DWT) based Mann-Kendall tests in central Gujarat region, India

Trend analysis has become one of the most important issues in hydro-meteorological variables study due to climate change and the focus given to it in the recent past from the scientific community. In this study, long-term trends of rainfall are analyzed in eight stations located in semi-arid central Gujarat region, India by considering time series data of 116 years (1901-2016). Discrete wavelet transform (DWT) as a dyadic arrangement of continuous wavelet transformation combined with the widely applied and acknowledged Mann-Kendall (MK) trend analysis method were applied for analysis of trend and dominant periodicities in rainfall time series at monthly, annual and monsoonal time scales. Initially, rainfall time series applied in this study were decomposed using DWT to generate sub-time series at high and low frequencies, before applying the MK trend test. Further, the Sequential Mann-Kendall (SQMK) test was also applied to find out the trend changing points. The result showed that at the monthly annual and monsoon time scales, the trends in rainfall were significantly decreasing in most of the station. The 4-month and 8-month components were found as dominant at the monthly time series and the 2-year and 4-year component were found as dominant at the monsoon time series, whereas the 2-year components were observed as dominant in the annual time scale.

An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm

Storing images consumes a lot of storage space due to the large number of bits used to represent them. These bits are comprised of pixels that make up the image. These heavy images are also very difficult to be transmitted over channels due to their great size. Compression involves the reduction of the amount of bits used in representing an image and consequently reducing the size of that image without losing any detail from the image. There are so many image compression techniques used to achieve this feat, but they have drawbacks such as lack of a model that can compress a satellite image, lack of adaptive reversible techniques for compression and inability to compress complex images such as satellite images. This work, proposed an hybrid Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DCT-DWT-SVD)-based techniques for satellite image compression. The algorithms were combined to breakdown the images into blocks/matrices and assign certain values to them depending on the concentration of colour bits around the region. The areas with higher bits are reduced and compression is achieved. A hybrid methodology of Agile and Waterfall model was used in this approach. The model was implemented using MATLAB and satellite images gotten from a public repository. The Compression ratio was 0.9990 and 0.9941 for the two images compressed which shows high and efficient compression. The Mean Square Error (MSE) was 2.51 which is low. This study will be beneficial to remote sensor companies, Graphic designers and the research community.

Digital Image Noise Estimation Using DWT Coefficients.

Noise type and strength estimation are important in many image processing applications like denoising, compression, video tracking, etc. There are many existing methods for estimation of the type of noise and its strength in digital images. These methods mostly rely on the transform or spatial domain information of images. We propose a hybrid Discrete Wavelet Transform (DWT) and edge information removal based algorithm to estimate the strength of Gaussian noise in digital images. The wavelet coefficients corresponding to spatial domain edges are excluded from noise estimate calculation using a Sobel edge detector. The accuracy of the proposed algorithm is further increased using polynomial regression. Parseval's theorem mathematically validates the proposed algorithm. The performance of the proposed algorithm is evaluated on a standard LIVE image dataset. Benchmarking results show that the proposed algorithm outperforms all other state of the art algorithms by a large margin over a wide range of noise.

Triplet Contents based Medical Image Retrieval System for Lung Nodules CT Images Retrieval and Recognition Application

Content Based Medical Image Retrieval (CBMIR) has found its relevance in medical diagnosis by processing massive medical databases based on visual and semantic features and user preferences. In this paper we address two issues such as retrieval and recognition. We present a novel method called Triplet-CBMIR for lung nodules CT images retrieval and recognition application. A Triplet CBMIR is a combination of three properties: Visual Features (Shape and Texture), Semantic Features and Relevance Feedback. Dataset training is done using: Preprocessing, Feature Extraction, Selection, Nodules Sign Detection and Clustering. In preprocessing we perform image scaling, denoising and normalization. In feature extraction, two methods are presented such as Hybrid Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), Bounding-Box based Convolutional Network (CNN) for visual and semantic features extraction. Then optimum set of feature vectors are selected using Mutual Information based Neighborhood Entropy (MINε). Based on selected features, lung nodule sign is detected using K-nearest Neighbor (KNN) algorithm in which Hassanat Distance used and similar images are grouped using Multi-Self organizing Map (SOM). For similarity measurement, d_1 distance metric is used. Benchmark dataset such as LISS and LIDC are used for the study. Performance matrices such as Average Precision Rate (APR), Average Retrieval Rate (ARR), Average Recognition Rate (ArR), Running Time found in the simulation results are compared with some other already present state-of-the-art works. The proposed method shows a significant improvement as compared to other existing methods.

An enhanced hybrid image watermarking scheme for security of medical and non-medical images based on DWT and 2-D SVD

In this paper, we proposed a blind image watermarking scheme to enhance the effectiveness of hybrid Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) based schemes using the second level of SVD. The foremost contribution of this paper is to eliminate the constraints of our previous proposed scheme based on DWT and 2-level SVD. In the previous scheme, the size of the watermark was dependent to the size of cover image. For example, for a cover image with the size of 512*512, the watermark should be changed to 64*64 because of the limitation in capacity. In the proposed scheme, we enhanced the scheme to be independent from the sizes of cover and watermark images as well as improving the robustness and imperceptibility. For this purpose, we presented image blocking to find optimum block size for embedding the watermark and to correct the enslavement of capacity with the host image size. To achieve these goals, an additional preprocessing is performed on the watermark image before watermark embedding. The watermark image will be duplicated redundantly to the size of the cover image. Then both cover and watermark images will be divided into non overlap 16*16 blocks. Also, security is ensured in this scheme by proposing a two-level authentication system for watermark extraction to detect both false positive and false negative problems. The proposed scheme is tested and certified for medical and non-medical images exposing on 10 types of signal processing and geometric attacks. Experimental result shows, significant enhancement in both imperceptibility and robustness with the average of 46.9496 db and more than 99% respectively, while image blocking makes the capacity independent from the size of the host image.

A novel hybrid DCT and DWT based robust watermarking algorithm for color images

In this paper, a novel robust color image watermarking method based on Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is proposed. In this method, RGB cover image is divided into red, green and blue components. DCT and DWT are applied to each color components. Grayscale watermark image is scrambled by using Arnold transform. DCT is performed to the scrambled watermark image. Transformed watermark image is then divided into equal smaller parts. DCT coefficients of each watermark parts are embedded into four DWT bands of the color components of the cover image. The robustness of the proposed color image watermarking has been demonstrated by applying various image processing operations such as rotating, resizing, filtering, jpeg compression, and noise adding to the watermarked images. Experimental results show that the proposed method is robust to the linear and nonlinear attacks and the transparency of the watermarked images has been protected.

A secure blind discrete wavelet transform based watermarking scheme using two-level singular value decomposition

In this paper, a secure blind image watermarking scheme is proposed to improve efficiency of hybrid Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) based schemes. The main contribution of this research is using two-level singular value decomposition in hybrid DWT + SVD watermarking schemes results to high improvement in both imperceptibility and robustness. Also, security is ensured in this scheme by proposing a two-level authentication system for watermark extraction in which not only the false positive problem is eliminated, when the attacks are very severe, the false negative effect is also detected and prevented. In the proposed scheme, the cover image is initially transformed to DWT and the high frequency sub bands are selected. Subsequently, High (HH) sub band is divided into 8 × 8 non overlapping blocks and the SVD transform is applied to each of them. In SVD transform, the highest energy compaction is in the first row and the first column of the matrix of singular values. These singular values for each block will be collected in a separate matrix and then the second SVD transform will be exerted on the produced matrix. The singular values of the watermark image after exerting one level DWT, is embedded into the singular values of this matrix. Experimental result shows when the scheme is subjected to ten most common types of signal processing and geometric attacks, using the second level of SVD makes it highly robust and imperceptible while it is independent from type of image. So, the proposed scheme is tested and certified for both medical and normal images. This scheme is compared to a range of hybrid schemes using DWT and one level SVD, and experimental results show high improvements in imperceptibility with the average of 46.6950 db and robustness more than 99% and proven efficiency in security.

Encoded Hybrid DWT based Watermarking Scheme based on Singular Matrix Decomposition

In the period of digital technology, digital information suffers from copyright and integrity violations. In case of any disputes like rights violation and content creator, digital watermark has a vital role to protect the property rights of the original owner of digital information. Numerous of watermarking techniques have been proposed recently but image quality of host image at receiving end is needed to be improved. We proposed Encoded Hybrid Digital Watermarking Scheme (EHDWS) to improve image quality which is based on Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD) and Bose ChaudhuriHocquenghem (BCH) code. DWT is used for decomposing the image into LL, HL, LH, HH sub-bands; SVD is used to decompose the image into orthogonal matrix (U), singular matrix (S), and inverse orthogonal matrix (V) and BCH code is used for error detection-correction, copyright protection and authentication. The proposed EHDWS has high degree of transparency, robustness, blindness. In EHDWS, watermark embedding scheme is used to encode the original watermark image by using BCH (7, 4) based algorithm for obtaining the encoded watermark image. Then embed this encoded watermark image into host image by using DWT-SVD based algorithm for obtaining the watermarked image. Watermark extracting scheme is used to obtain both extracted watermark image for authentication purpose and extracted host image from watermarked image to obtain original image. PSNR values are also calculated to know the quality of watermarked image. General Terms Image security, watermarking.

Comparison of Multi-View Face Recognition using DCT and Hybrid DWT of Score Fusion under Uncontrolled Illumination Variation

A facial recognition system is one of the biometric applications for automatically identifying or verifying a person from a digital image or a video frame from a video source. Now a day’s face recognition system is recognize the face using multiple-views of faces, for detecting each view of face such as left, right, front, top, and bottom. The proposed face recognition system consists of a novel illuminationinsensitive preprocessing method, a hybrid discrete Wavelet Transform (HDWT) for feature extraction and Density based Score Fusion Technique for matching. First, in the preprocessing stage different stages are followed like Gamma Correction, DOG(Difference of Gaussion)filtering and contrast equalization which transforms, a face image into an illumination-insensitive image, Then, for feature extraction of complementary classifiers, multiple face models based upon HDWT are applied. To do the feature extraction this system uses DCT Wavelet transform to generate the feature vectors of the query and database images. Euclidean distance is used as similarity measure to compare the image. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system uses the ORL face database, and compare with DCT method. Using ORL database the Hybrid wavelet has achieves smaller size feature vectors which is performing better with 98.55% accuracy and less computation time require for recognizing image which is 19.0645 msec to 24.7653 msec .Which is less as compare to DCT method, DCT method achieves 96.45% accuracy and computational time 26.2349 msec to 33.0234 msec which is more as compare to proposed method..

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images.

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction.