Finite Ridgelet Research Articles

Color Image Watermarking Using Adaptive Tunable Q Wavelet, Finite Ridge Let, and Arnold Transforms with TSVD

In recent years, researchers have been working to integrate watermarking practices anchored in wavelet transform (WT) coefficients and Singular value decomposition (SVD) to recover the robustness of watermarked images in opposition to geometric and non-geometric attacks. Conversely, the watermark embedding, can initiate vagueness in the false-positive problem (FPP). To address this issue, Flamingo search algorithm-based Color Image Watermarking using Adaptive Tunable Q wavelet, Finite ridge let, Arnold transforms with Truncated Singular value decomposition (ATQWT-FRT-AT-TSVD-FSOA-CIW) is proposed. The proposed ATQWT-FRTAT-TSVD-FSOA-CIW method is separated into two stages: (i) watermark embedding and (ii) watermark extraction. The scaling factor is optimized with the help of the Flamingo search algorithm, which provides higher robustness, higher embedding strength, resistance to a false-positive problem, and better quality of the watermarking scheme. The proposed ATQWT-FRT-AT-TSVD-FSOA-CIW method is simulated in MATLAB tool. The performance of the proposed ATQWT-FRT-AT-TSVD-FSOA-CIW design is estimated under several performance metrics, such as Peak signal to noise ratio (PSNR), Structural similarity index measurement (SSIM), and Normalized cross-correlation (NCC). The performance of the proposed ATQWT-FRT-AT-TSVD-FSOA-CIW method attains 35%, 22.43%, 21.157%, 13.96%, and 7.186% higher PSNR compared to the existing designs, like secure with robust color image watermarking utilizing nature-inspired intelligence (DWT-SVD-ABCOA-CIW), the Artificial bee colony basis perceptually tuned blind color image watermarking in the hybrid LWT-DCT domain (LWT-DCT-ABCOA-CIW), the DWT-SVD based adaptive color multi-watermarking scheme for copyright protection under AMEF and PSO-GWO (DWT-SVD-Hybrid PSO-GWOA-CIW), Robust image watermarking based on fractional Krawtchouk transform with optimization (FrKT-Hybrid FO-CSOA-CIW), and the optimized semi-blind scheme for color image watermarking (DWTFRT-AT-SVD-PSO-CIW).

A Robust and Imperceptible Dark Frame Selection Using Advanced Squeezed Convoluted Ebola Neural Network-Based Video Watermarking Scheme with All Phase Discrete Cosine Biorthogonal Transform

Watermarking has been developed as a beneficial approach to resolve security problems such as copyright defense, legitimate ownership, and authenticity of digital data. Grayscale or binary images and color images are the most important types of embedded watermarks in the existing video watermarking schemes, while dark frame watermarking is barely used. A new robust blind video watermarking system with an Advanced Squeezed Convoluted Ebola Neural Network (ASCENN) approach is suggested to secure copyright, reduce noise, and authenticate the authenticity of dark frames. The proposed method selects the dark frames from the extracted input video. Then the selected dark frames are embedded and extracted using Finite Ridgelet Transform (FRT), All Phase Discrete Cosine Biorthogonal Transform (APDCBT), and Singular Value Decomposition (SVD) techniques. The robustness of the introduced approach is established by evaluating it through various image watermarking techniques and evaluating its performance against geometric, nongeometric, and combinational attacks. While comparing with the other existing video watermarking approaches, the proposed method has obtained a maximum correlation coefficient of 1, and the signal-to-noise ratio of 75[Formula: see text]dB, at minimum error rate of 0.0215, respectively.

A robust and high embedding capacity watermarking technique for telemedicine

ABSTRACT The medical field has been greatly influenced by the rapid expansion of technology and globalization. Medical images are routinely transferred by wired or remote media amongst professionals, medical experts, analysts, and patients themselves in order to improve the diagnostic outcomes. To achieve investigative conclusions, physicians concentrate on the ROI (Region of Interest) in a medical image. To retrieve the ROI when it has been damaged by noise or altered by intruders during transmission, the ROI data are hidden within the RONI (Region of Non-Interest) in medical images. This paper intends a high embedding capacity watermarking system grounded on Finite Ridgelet Transform (FRT). Proposed method supports the high embedding capacity for concealing entire ROI data inside RONI and simultaneously provides a high level of robustness to the concealed data.

Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection.

Every year, cervical cancer is a leading cause of mortality in women all over the world. This cancer can be cured if it is detected early and patients are treated promptly. This study proposes a new strategy for the detection of cervical cancer using cervigram pictures. The associated histogram equalization (AHE) technique is used to improve the edges of the cervical image, and then the finite ridgelet transform is used to generate a multi-resolution picture. Then, from this converted multi-resolution cervical picture, features such as ridgelets, gray-level run-length matrices, moment invariant, and enhanced local ternary pattern are retrieved. A feed-forward backward propagation neural network is used to train and test these extracted features in order to classify the cervical images as normal or abnormal. To detect and segment cancer regions, morphological procedures are applied to the abnormal cervical images. The cervical cancer detection system's performance metrics include 98.11% sensitivity, 98.97% specificity, 99.19% accuracy, a PPV of 98.88%, an NPV of 91.91%, an LPR of 141.02%, an LNR of 0.0836, 98.13% precision, 97.15% FPs, and 90.89% FNs. The simulation outcomes show that the proposed method is better at detecting and segmenting cervical cancer than the traditional methods.

Color Ultrasound Image Watermarking Scheme Using FRT and Hessenberg Decomposition for Telemedicine Applications

Watermarking is a valuable technique for verifying medical images obtained through the internet for diagnosis. There is a greater need for security in medical pictures with ever- increasing security risks. This research presented a Finite Ridgelet Transform (FRT)-Hessenberg based watermarking scheme in medical images. The suggested paradigm is divided into two stages. Before watermark insertion, FRT is applied to medical images. The coefficients are combined into blocks of 4 x 4 and each block is decomposed using Hessenberg decomposition. The second column of the Q matrix is used to insert the watermark using the additive quantization technique. The results obtained from our experiment have given good visual quality of the watermarked images. The high PSNR value 53.6121 and NC value 1.0 show that our scheme is performing better. Moreover, the performance of our scheme is robust against several attacks. The consequences of this result imply that the anticipated scheme is effective for medical image watermarking.

Research on intelligent prediction of hydrogen pipeline leakage fire based on Finite Ridgelet neural network

In order to effectively predict fire risk of hydrogen pipeline leakage and improve the safe level of hydrogen pipeline, the finite Ridgelet neural network optimized by improved firefly algorithm is constructed. Related researches on pipeline leakage fire risk and corresponding prediction models are summarized. The finite Ridgelet neural network is constructed by using finite Ridgelet function as excitation function of node in hidden layer, and the structure of finite Ridgelet neural network is established. The improved firefly algorithm based on location update mechanism is designed to optimize the parameters of finite Ridgelet neural network. And then the fire risk level prediction procedure of hydrogen pipeline is designed. Finally, forty training samples and ten testing samples are selected to carry out fire risk level prediction analysis, results show that the proposed finite Ridgelet neural network optimized by improved firefly algorithm has advantages in prediction precision and accuracy, which can effectively predict the safety status of hydrogen pipeline.

Fired bullet signature correlation using the finite ridgelet transform (FRIT) and the gray level co-occurrence matrix (GLCM) methods

When a bullet is fired from a barrel, micro striation marks caused by the sliding motion of the bullet through the rifled barrel are one of the foremost factors in automated ballistic identification. This paper focuses on 3D topography images of land engraved areas (LEA) and proposes a bullet identification method incorporating the finite ridgelet transform (FRIT) and gray level co-occurrence matrix (GLCM) algorithms. The FRIT extracts the striation marks from the 3D micro image and the GLCM generates a linearly weighted weight corresponding to the texture features for 2D average profile calculation. The entire striation marks image is divided into several cells and a cell with valid correlation areas is assigned a large weight, but the one with invalid correlation areas is assigned a small weight along the vertical direction. The visible results show that the valid correlation areas are effectively identified and the negative effects of invalid correlation areas are suppressed. Tests were performed on a control set and an unknown set, giving a total of 35 bullet samples fired from pistols with 10 consecutively manufactured slides. The results included no false identifications or false exclusions and a clear separation between the matching index of the matching and non-matching LEA profiles, demonstrating excellent performance in striation mark capture and valid correlation areas extraction of FRIT and GLCM algorithms. The proposed method is capable of correctly matching toolmarked surfaces to the barrel used.

A Novel Optimized Semi-Blind Scheme for Color Image Watermarking

Image watermarking is a robust solution for solving key issues like copyright protection and proof of ownership of digital data. Existing schemes of image watermarking mostly used grayscale or binary images as embedded watermarks, while only a few watermarking schemes are developed for color images. In this article, we propose a novel robust semi-blind image watermarking scheme based on finite ridgelet transform (FRT), discrete wavelet transform (DWT), singular value decomposition (SVD), particle swarm optimization (PSO), and Arnold transform to protect copyright and verify the authenticity of color images.Firstly, the color image is converted from RGB to YCbCr color space, and the luminance component (Y) is taken into account to insert the watermark data. In this study, the principal component (PC) of the watermark image is directly inserted into the corresponding singular value of the cover image by the scaling factor to avoid the false positive problem (FPP). To further improve security, Arnold transform is applied to process the Y channel of the watermark image before inserting it in the cover image. Besides, PSO optimizes the embedding factor matrices.The qualitative evaluation factors like peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are used to assess the visual quality, while normalized correlation coefficient (NCC) is used to assess the resemblance between the watermarked and the restored watermarked images. The performance of the proposed scheme is evaluated using geometric, non-geometric, and combinational attacks, and its comparison is performed with different image watermarking schemes to prove its robustness.

A FRT - SVD Based Blind Medical Watermarking Technique for Telemedicine Applications

In this article, a blind and robust medical image watermarking technique based on Finite Ridgelet Transform (FRT) and Singular Value Decomposition (SVD) is proposed. A host medical image is first transformed into 16 × 16 non-overlapping blocks and then ridgelet transform is applied on the individual blocks to obtain sets of ridgelet coefficients. SVD is then applied on these sets, to obtain the corresponding U, S and V matrix. The watermark information is embedded into the host medical image by modification of the value of the significant elements of U matrix. This proposed technique is tested on various types of medical images such as X-ray and CT scan. The simulation results revealed that this technique provides better imperceptibility, with an average PSNR being 42.95 dB for all test medical images. This technique also overcomes the limitation of the existing technique which is applicable on only the Region of Interest (ROI) of the medical image.

Blind image separation using pyramid technique

Signal and image separation is an important processing step for accurate image reconstruction, which is increasingly applied to many medical imaging applications and communication systems. Most of the conventional separation approaches are based on frequency domain and time domain. These approaches, however, are sensitive to noise and thus often produce undesirable results.In this paper, we propose a novel method of image separation. It incorporates the property of pyramid component extracted from the image and a finite ridgelet transform (FRT) to obtain a precise analysis of the images and thus correctly separate the images even in a highly noisy environment. We obtain the multiple components of the target images by employing a pyramid processing, which operates in the various domains and thus can decompose the image into multiple components.In addition, the pyramid decomposition in the proposed method can eliminate information redundancy in the target image and thus can substantially enhance the quality of image separation. We have conducted extensive simulations, which demonstrate that the proposed pyramid structure with FRT outperforms the conventional methods based on time domain and trigonometric transforms.

A color image steganography in hybrid FRT–DWT domain

In this paper, a color image steganography technique based on Finite Ridgelet Transform (FRT) and Discrete Wavelet Transform (DWT) is proposed. The FRT is applied on the cover color image to get Ridgelet coefficients of each color channel of cover color image and a single level DWT is applied to get different wavelet coefficients, which are further modified by encrypted channel values of secret color image to get stego color image. In the proposed method, Arnold scrambling is used to encrypt channels of secret color image. The proposed technique is tested for its effectiveness on various types of standard color images and the results showed improved imperceptibility of the stego image compared to the existing technique. Further, the embedding capacity of the proposed technique is high.

Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

This work offers an approach to aerial image classification for use in remote sensing object recognition, image processing and computer vision. Sparse coding (SC) is used to classify unmanned-aerial-vehicle (UAV) and satellite images because SC representation can generalize a large dataset and improve the detection of distinctive features by reducing calculation time for feature matching and classification. Features from images are extracted based on the following descriptors: (a) Scale Invariant Feature Transform; (b) Histogram of Oriented Gradients; and (c) Local Binary Patterns. SC representation and local image features are combined to represent global features for classification. Features are deployed in a sparse model to store descriptor features using extant dictionaries such as (a) the Discrete Cosine Transform and (b) the Discrete Wavelet Transform. An additional two dictionaries are proposed as developed for the present work: (c) the Discrete Ridgelet Transform (DRT) and (d) the Discrete Tchebichef Transform. The DRT dictionary is constructed by using the Ricker wavelet function to generate finite Ridgelet transforms as basis elements for a hybrid dictionary. Different pooling methods have also been employed to convert sparse-coded features into a feature matrix. Various machine learning algorithms are then applied to the feature matrix to classify objects contained in UAV and satellite imagery data. Experimental results show that the SC model secured better accuracy rates for extracted discriminative features contained in remote sensing images. The authors concluded that the proposed SC technique and proposed dictionaries provided feasible solutions for image classification and object recognition.

A Blind Restoration Approach for Defocused Barcode Images

Use of a mobile camera for barcode decoding provides high portability and availability but it requires that the recorded barcode image must be accurate representation of the barcode that is available on the product. Barcode scanning is challenging because images may be degraded due to out-of-focus blur at the time of image acquisition. Therefore, image restoration is essential in making image sharp and useful. In case of blind restoration of such barcode images accurate estimation of out-of-focus blur parameter is highly desirable. In this article, a robust method has been proposed for estimating the radius of out-of-focus blur. Finite discrete ridgelet transform has been used to find the features of the blurred image and a radial basis function neural network is utilized to estimate the radius of out-of-focus blur. The experimental results reveal that proposed method more robust than the existing methods.

Efficient Source Separation Enhancement Based on Advanced Multi-dimensional Transform Technique

This paper is concerned with blind separation of digital images from mixtures. It suggests the implementation of a blind separation technique on the ridgelet transform (RT) of the mixed images, instead of executing the separation on the mixtures in the time or a trigonometric transform domain. Ridgelet transform is a new orientational multi-resolution transform, and it is widely appropriate for characterizing the signals with dimensional singularities. Finite ridgelet transform is a discrete implementation of the ridgelet transform, which has numerical accuracy as the uninterrupted RT and has soft calculation intricacy. In contrary to the time domain, the RT finds more applications in image separation, because it appears sleek and edge sides of images have sparsity. In addition, the RT involves additional orientation information. The separated images are obtained using independent component analysis. The simulation results reveal that image separation in the RT domain is better, when compared to separation in the time or trigonometric transform domains.

Application of Genetic Algorithm and Particle Swarm Optimization techniques for improved image steganography systems

Abstract Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT) and Finite Ridgelet Transform (FRIT) are used in combination with GA and PSO to improve the efficiency of the image steganography system.

Multispectral palmprint recognition methodology based on multiscale representation

The increasing demand to develop a palmprint biometric system with a low-error rate has prompted scientists to use multispectral imaging to overcome the limits of the techniques that act in visible light. In order to improve the accuracy of multispectral palmprint recognition, we explore two level fusions: pixel and the feature level fusion approaches. The former is based on a maximum selection rule, which combines discriminating information from different spectral bands of discrete wavelet transform of multispectral images. The latter operates the fusion of features extracted from subimages. We propose to use both approaches for statistical and energy distribution analysis of the finite ridgelet transform coefficients, for the sake of their simplicity and low-computational complexity. Once the feature vectors are obtained, we perform a robust classification to identify/verify individuals with both approaches. The effectiveness of the proposed methods is evaluated on several classifiers for binary and multiclass cases. The experimental results conducted on Chinese Academy of Sciences Institute of Automation and Hong Kong Polytechnic University databases show that the proposed approaches ensure, respectively, an accuracy rate of 100% and 99.79%. A comparative study has revealed that our approach outperforms or at least equals the performances of the state-of-the-art multispectral palmprint recognition methods.

FFRT: A Fast Finite Ridgelet Transform for Radiative Transport

This paper introduces an FFT-based implementation of a fast finite ridgelet transform which we call FFRT. Inspired by recent work where it was shown that ridgelet discretizations of linear transport equations can be easily preconditioned by diagonal preconditioning, we use the FFRT for the numerical solution of such equations. Combining this FFRT-based method with a sparse collocation scheme, we construct a novel solver for the radiative transport equation which results in uniformly well-conditioned linear systems.

Patch based image denoising using the finite ridgelet transform for less artifacts

Patch based denoising methods have proved to lead to state-of-the-art results. However, in contrast with intensive pursuing of higher peak signal to noise ratio (PSNR), less attention is paid to visual quality improvement of denoised images. In this paper, we first compare the denoising performance in edge and smooth regions. Results reveal that edge regions are the main source for potential performance improvement. This motivates us to investigate the use of the finite ridgelet transform as a local transform for better preservation of directional singularities. A two stage denoising algorithm is then proposed to improve the representation of detail structures. Experimental results in denoising images which only contain white noise show that the proposed algorithm consistently outperforms other methods in terms of PSNR and Structural SIMilarity index. Denoised images by the proposed method also demonstrate good visual quality with the least artifacts and fake structures in experiments on natural images.

A 4-quadrant Curvelet Transform for Denoising Digital Images

The conventional discrete wavelet transform (DWT) introduces artifacts during denoising of images containing smooth curves. Finite ridgelet transform (FRIT) solved this problem by mapping the curves in terms of small curved ridges. However, blind application of FRIT all over an image is computationally heavy. Finite curvelet transform (FCT) selectively applies FRIT only to the tiles containing small portions of a curve. In this work, a novel curvelet transform named as 4-quadrant finite curvelet transform (4QFCT) based on a new concept of 4-quadrant finite ridgelet transform (4QFRIT) has been proposed. An image is band pass filtered and the high frequency bands are divided into small non-overlapping square tiles. The 4QFRIT is applied to the tiles containing at least one curve element. Unlike FRIT, the 4QFRIT takes 4 sets of radon projections in all the 4 quadrants and then averages them in time and frequency domains after denoising. The proposed algorithm is extensively tested and benchmarked for denoising of images with Gaussian noise using mean squared error (MSE) and peak signal to noise ratio (PSNR). The results confirm that 4QFCT yields consistently better denoising performance quantitatively and visually.

Image compression approach with ridgelet transformation using modified neuro modeling for biomedical images

Image compression is applied to many fields such as television dissemination, remote sensing, image storage. Digitized images are compressed by a method which exploits the redundancy of the images so that the number of bits required to represent the image can be reduced with acceptable degradation of the decoded image. The humiliation of the image quality is limited with respect to the application used. There are various biomedical applications where accuracy is of major concern. To attain the objective of performance improvement with respect to decoded picture quality and compression ratios, in contrast to existing image compression techniques, an effective image coding technique which involves transforming the image into another domain with ridgelet function and then quantizing the coefficients with hybrid neural networks combining two different learning networks called auto-associative multilayer perceptron and self-organizing feature map is proposed. Ridge functions are effective in representing functions that have discontinuities along straight lines. Normal wavelet transforms not succeed to represent such functions effectively. The results obtained from the combination of finite ridgelet transform with hybrid neural networks found much better than that obtained from the JPEG2000 image compression system.