Wavelet Image Research Articles

A Coverless Image Steganography Based on Huffman Encoding and Robust Image Wavelet Hashing

Data hiding approaches are crucial in insecure communication to protect sensi- tive information from illegal access. The robustness of existing coverless image steganogra- phy methods that utilize mapping rules is derived from their ability to extract feature patterns within the spatial domain. In order to protect sensitive data more securely and to increase its durability against attacks, a new coverless steganography is introduced in this study. The me- thod used is based on the frequency domain. A coverless image steganography based on ro- bust image wavelet hashing and Huffman encoding. Initially, the confidential data is encoded through lossless compression using Huffman Encoding, which reduces the payload and en- hances the capacity for embedding. Second, segments of an 8-bit size are created from the compressed secret data. The efficient DWT hashing technique has been employed to generate a hash sequence for an image. An inverted index structure is established, allowing for the se- lection of the image whose hash corresponds to the secret data segment. Subsequently, all chosen images along with supplementary information are transmitted to the recipient. Testing has shown that the proposed method is robust against various image processing attacks, in- cluding scaling, low pass filtering, JPEG compression, rotation, noise introduction, and me- dian/mean filtering. Research findings and analysis indicate that this method enhances the latest coverless steganography algorithms in terms of capacity, resilience, and security.

SEGMENTATION OF DIGITAL IMAGES WITH WAVELET TRANSFORMATION USING MATLAB VERSION R2010B

The purpose of this research is to explore and apply the use of wavelet transformation for the segmentation of digital images, utilizing MATLAB version R2010B. The study aims to analyze how wavelet transform can be used to enhance the accuracy and effectiveness of image segmentation, which is a critical process in image processing and computer vision. The research contributes to the field of digital image processing by demonstrating the application of wavelets transformation for segmenting digital images. In this study, the study provides insights into how wavelets can be utilized to improve the detection of image features, especially in identifying image features more accurately. The experimental results show that only the Canny operator's edge detection method has the best edge detector in detecting the edges of objects in wavelet images. The technique of determining the threshold value (thresholding) can be carried out in two ways, namely, automatic method and technique carried out by trial and error. Finally, improving automatic thresholding techniques using AI-driven algorithms to reduce the reliance on trial-and-error methods could provide more consistent results in varied application areas.

Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction.

The advancement of 6G (6th Generation Mobile Networks) communication technology has posed challenges for traditional communication network architectures in meeting the demands for communication efficiency and quality. Semantic communication technology, characterized by its "understand before transmit" approach, has emerged as a pivotal technology driving the progress of 6G due to its ability to enhance communication efficiency and quality. The Wireless Image Transmission Transformer (WITT) model, which operates as a semantic communication system leveraging vision transformer technology for the transmission of semantic images, has shown efficacy in transmitting input images through processes of feature extraction and channel adaptation. This study introduces an advanced channel adaptive module that is informed by deep learning methodologies and the adaptive modulation principles of the Variational Information Bottleneck (VIB). This innovation enhances the original WITT model, resulting in the development of the Adaptive Wireless Image Transmission Transformer (ADWITT) architecture. Comprehensive experimental results have unequivocally shown that the transmission performance of the ADWITT architecture substantially surpasses that of the conventional WITT (Wavelet Image Transmission Technique) model, particularly in scenarios characterized by harsh and detrimental channel conditions. These findings underscore the robustness and adaptability of the ADWITT approach, which is poised to improve the field of image transmission by offering superior performance and resilience in environments where traditional methods falter.

Identification of Disc Cutter Wear via Operation Parameters Combined With Vibration Data: A Case Study

ABSTRACTThis paper proposed an approach to estimate disc cutter wear utilizing a combination of multiple operational parameters and vibration data collected during shield tunneling operations. The incorporation of vibration signals, notably those originating from acceleration sensors mounted on the back plate of the soil chamber, has markedly enhanced the accuracy of the model. Time‐frequency domain features were extracted through analysis methods such as Fast Fourier Transform (FFT), Short‐Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT). A predictive model utilizing vibration and shield operation parameters was developed using the XGBoost algorithm, and a deep GoogLeNet Convolutional Neural Network (CNN) was trained on time‐frequency graphs from the CWT. In addition, this study also investigated the impact of signal duration on wavelet image information and model accuracy. In the Huang‐Shang Intercity Railway Project, the approach effectively assessed disc cutter wear during tunneling operations and dynamically optimized the operational parameters of the shield tunnel machine through predictive analysis.

SFNet: Stellar Feature Network with CWT for Stellar Spectra Recognition

Stellar spectral classification is crucial in astronomical data analysis. However, existing studies are often limited by the uneven distribution of stellar samples, posing challenges in practical applications. Even when balancing stellar categories and their numbers, there is room for improvement in classification accuracy. This study introduces a Continuous Wavelet Transform using the Super Morlet wavelet to convert stellar spectra into wavelet images. A novel neural network, the Stellar Feature Network, is proposed for classifying these images. Stellar spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR9, encompassing five equal categories (B, A, F, G, K), were used. Comparative experiments validate the effectiveness of the proposed methods and network, achieving significant improvements in classification accuracy.

Diagnostics of Ship Engines Based on Wavelet Neural Network and Image Scanning Using Programmable Logic Circuit

The article is devoted to a diagnostic system for ship engines based on a wavelet neural network and image scanning using a programmable logic circuit and considers a method for analysing multifractal wavelet models. The combination of wavelet neural networks with a programmable PLIC-based (programmable logic integrated circuit) real-time image processing platform has a significant potential for the purposes of non-destructive testing, which makes it possible to accurately diagnose faults and take effective measures for predictive maintenance, which in turn makes it possible to effectively increase safety and reliability of equipment and reduce maintenance costs. The article proposes an improved approach to the diagnosis of ship engines, which is based on a wavelet neural network and image scanning using a programmable logic circuit. Wavelet packet decomposition is a method for local time and frequency analysis. It gradually refines the signal at multiple scales through scaling and conversion operations, and it can automatically adapt to the requirements of time-frequency signal analysis to focus on any detail of the signal. It has the advantage of good diagnostic accuracy for information with different noise levels, as well as high reliability since image data from multiple engine signals is used.

A new approach based on 2D wavelet transform in the analysis of wettability behavior and evaporation rate for the surface of PTFE dielectric material used in high voltage applications

In this study, a new approach based on 2D wavelet transform is proposed for the analysis of wettability behavior and evaporation rate. Droplet images were taken from the PTFE dielectric sample surface at 30th, 270th, 510th, 750th, 990th, 1230th, 1470th, and 1710th seconds. The droplet images were decomposed into sub-bands using functions from six different wavelet families. The energy, entropy, mean, standard deviation, skewness, and kurtosis measures were calculated using wavelet coefficients for each sub-band image. Linear, exponential, and polynomial fitting methods were used to estimate the contact angle of the droplet images. The energy and standard deviation values, which are the selected optimal measures, were used as input in the fitting methods, and the regression coefficient (R2) and Root Mean Square Error (RMSE) values of the fitting methods were calculated. Thus, a high-performing contact angle prediction model with 2nd level sub-band image wavelet coefficients has been obtained.

Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data

For the poor model generalization and low diagnostic efficiency of fault diagnosis under imbalanced distributions, a novel fault diagnosis method using variational autoencoder generation adversarial network and improved convolutional neural network, named VGAIC-FDM, is proposed in this paper. First, to capture local features of vibration signals, continuous wavelet transform is employed to convert the original one-dimensional fault signals into wavelet time–frequency images. Second, for the data dimensionality reduction and model simplification, the time–frequency wavelet images are processed in grayscale to generate single-channel grayscale time–frequency images. Then, sample augmentation is performed on grayscale time–frequency images to balance the dataset by using a variational autoencoder generation adversarial network. Finally, the generated images and the original images are fused and trained by using a focus-loss-optimized CNN classifier to achieve fault diagnosis under unbalanced conditions. The experimental results show that the VGAIC-FDM effectively captures the potential spatial distribution of real samples and alleviates the impact caused by the inconsistent difficulty of sample classification. As a result, it enhances the fault diagnosis performance of the model when dealing with unbalanced datasets, leading to higher accuracy and F1-score values.

Deep learning-based acoustic emission data clustering for crack evaluation of welded joints in field bridges

To advance the intelligent operation and maintenance of bridges, a deep learning-based acoustic emission (AE) data clustering framework was developed for evaluating fatigue cracks in welded joints under conditions of operational noise interference and complex damage mechanisms. Specifically, a convolutional autoencoder (CAE) model was implemented to extract damage-sensitive features from AE wavelet images. Additionally, a physics-guided single-and-cross-case strategy using Gaussian mixture models (GMMs) was presented to diagnose overlapping microscopic noise and damage mechanisms across different cases with various crack lengths. Field tests demonstrated the efficiency of the proposed framework to distinguish AE data induced by noise, crack propagation, surface fretting, and impact, enabling accurate identification of no-damage, minor-damage, and serious-damage cases according to their characteristic mechanisms. Future work will incorporate long-term monitoring data from additional cases to further refine the damage quantification and enhance the overall robustness.

An Online Monitoring Approach of Carbon Steel Corrosion via the Use of Electrochemical Noise and Wavelet Analysis

In this study, carbon steel was examined under different corrosive conditions using electrochemical noise (EN) as the primary method of investigation. The corroded carbon steel surfaces were examined using 3D profilometry to gather information about localized defects (pits). A post-EN analysis approach was used using the discrete wavelet transform (DWT) method, which emphasizes the necessity of employing wavelet analysis as a quantitative analysis approach for electrochemical noise. A well-established approach to extract features from wavelet scalogram images, based on the concept of local binary patterns (LBPs), was used to extract features from these wavelet images. The results demonstrated that electrochemical noise associated with wavelet transform analysis, particularly wavelet scalograms, is an effective tool for monitoring the localized corrosion of carbon steel.

Soft X-ray image recognition and classification of maize seed cracks based on image enhancement and optimized YOLOv8 model

The current investigation on image recognition and internal crack detection of maize seeds primarily relies on visible light imaging. However, due to the low transmissivity of plant cells, even with image enhancement measures, the clarity of internal cracks in the images and the subsequent feature extraction process can be a trade-off. Soft X-rays possess exceptional penetration capability and offer better safety and convenience compared to hard X-rays, making them highly suitable for visualizing internal structures within plant tissues like maize seeds. In this paper, a non-invasive Imaging Technique for Image Enhancement is proposed, combining wavelet thresholding denoising, image standardization, bilateral filtering, and laplacian sharpening. This method is based on soft X-rays and successfully achieves image recognition of cracks present inside Zhengdan 958 maize seeds using an optimized YOLOv8 model. It effectively addresses challenges related to the limited light transmission of maize seeds, difficulty in crack localization, and algorithm generalization issues. The optimized YOLOv8 model demonstrates an average precision (AP) value that is 3.1% higher than that of the original model. Furthermore, by applying image enhancement, the AP value increases by 1.8%. The proposed method exhibits an average recognition accuracy of 99.66% for intact or broken seeds, an average precision of 99.87%, an average recognition recall of 99.48%, and an average single-frame image detection time of 7.49 ms in the single seed detection experiment.

Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks

The Intelligent Fault Diagnosis of rotating machinery calls for a substantial amount of training data, posing challenges in acquiring such data for damaged industrial machinery. This paper presents a novel approach for generating synthetic data using a Generative Adversarial Network (GAN) with cycle consistency loss function known as cycleGAN. The proposed method aims to generate synthetic data that could effectively replace real experimental data. The generative model is trained to transform wavelet images of simulated vibrational signals into authentic data obtained from machinery with damaged bearings. The utilization of Maximum Mean Discrepancy (MMD) and Fréchet Inception Distance (FID) demonstrates a noteworthy resemblance between synthetic and real experimental data. Also, the generative model enables the synthesis of data that may have been entirely lacking from the experimental observation, indicating generative zero-shot learning capabilities. The efficacy of synthetic data in training diagnosis algorithms by means of Transfer Learning (TL) on Convolutional Neural Networks (CNNs) has been demonstrated to be comparable to that of real data. The study has been validated by means of the test rig for medium-sized industrial bearings accessible at the Politecnico di Torino.

High-Speed Wavelet Image Processing Using the Winograd Method with Downsampling

Wavelets are actively used to solve a wide range of image processing problems in various fields of science and technology. Modern image processing systems cannot keep up with the rapid growth in digital visual information. Various approaches are used to reduce the computational complexity and increase computational speeds. The Winograd method (WM) is one of the most promising. However, this method is used to obtain sequential values. Its use for wavelet image processing requires expanding the calculation methodology to cases of downsampling. This paper proposes a new approach to reduce the computational complexity of wavelet image processing based on the WM with decimation. Calculations have been carried out and formulas have been derived that implement digital filtering using the WM with downsampling. The derived formulas can be used for 1D filtering with an arbitrary downsampling stride. Hardware modeling of wavelet image filtering on an FPGA showed that the WM reduces the computational time by up to 66%, with increases in the hardware costs and power consumption of 95% and 344%, respectively, compared to the direct method. A promising direction for further research is the implementation of the developed approach on ASIC and the use of modular computing for more efficient parallelization of calculations and an even greater increase in the device speed.

다중채널 CNN-LSTM 및 웨이브렛 이미지 기반 전력계통 이벤트 분류

다중채널 CNN-LSTM 및 웨이브렛 이미지 기반 전력계통 이벤트 분류

다중채널 CNN-LSTM 및 웨이브렛 이미지 기반 전력계통 이벤트 분류

다중채널 CNN-LSTM 및 웨이브렛 이미지 기반 전력계통 이벤트 분류

Improved wavelet prediction superresolution reconstruction based on U‐Net

AbstractDeep learning can be used to achieve single‐image superresolution (SR) reconstruction. To address problems encountered during this process, such as the number of network parameters, high training requirements on equipment performance, and inability to downsample certain SR images accurately, an image SR reconstruction algorithm based on deep residual network optimization is proposed. The model introduces wavelet transforms based on the original U‐Net, where the U‐Net is trained to obtain SR wavelet feature images at multiple scales simultaneously. This approach reduces the mapping space for the network to learn low‐ to high‐resolution image mapping, which in turn reduces the training difficulty of the model. In terms of network details, the inverse wavelet transform is used in image upsampling to enhance the sparsity of the reconstruction layer in the original network. The network structure of the U‐Net upsampling is adjusted slightly to enable the network to distinguish wavelet images from feature images, thereby improving the richness of the features extracted by the model. The experimental results show that the peak signal‐to‐noise ratio (PSNR) of the fourfold SR model is 32.35 and 28.68 on the Set5 and Set14 validation sets, respectively. Compared with networks that use wavelet prediction mechanisms, such as the deep wavelet prediction SR (DWSR) and deep wavelet prediction‐based residual SR (DWRSR) models, the PSNR for all the tested public datasets is improved by 0.5. The method yields superior results in terms of both visual effect and PSNR, demonstrating the feasibility of the wavelet prediction mechanism in SR reconstruction and thus offering application value and research significance.

Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals.

The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision medicine, particularly in disease prevention, detection, and personalized treatment. This study aims to determine the optimal combination of the mother wavelet and AI model for the analysis of pediatric electroretinogram (ERG) signals. The dataset, consisting of signals and corresponding diagnoses, undergoes Continuous Wavelet Transform (CWT) using commonly used wavelets to obtain a time-frequency representation. Wavelet images were used for the training of five widely used deep learning models: VGG-11, ResNet-50, DensNet-121, ResNext-50, and Vision Transformer, to evaluate their accuracy in classifying healthy and unhealthy patients. The findings demonstrate that the combination of Ricker Wavelet and Vision Transformer consistently yields the highest median accuracy values for ERG analysis, as evidenced by the upper and lower quartile values. The median balanced accuracy of the obtained combination of the three considered types of ERG signals in the article are 0.83, 0.85, and 0.88. However, other wavelet types also achieved high accuracy levels, indicating the importance of carefully selecting the mother wavelet for accurate classification. The study provides valuable insights into the effectiveness of different combinations of wavelets and models in classifying ERG wavelet scalograms.

Solar H[formula omitted] image interference fringes removal based on stationary wavelet deep neural network

The solar Hα images acquired by the Goode Solar Telescope (GST) at the Big Bear Solar Observatory (BBSO) are superimposed by interference fringes. These fringes degrade the image quality and seriously affect later research that uses these data. To remove the interference fringes in Hα images, this paper proposed a method combining stationary wavelet deep convolutional neural network (SWT-CNN) and image pyramid. First, the image pyramid was used to downsample the fringe image, which sped up the neural network training speed. Then, SWT-CNN used the difference between fringe image and fringe-free image to remove the interference fringe. Finally, the image size was recovered by the Laplacian pyramid. The experimental results show that our method can effectively remove the interference fringes in Hα images and preserve the solar structure.

Robust quantitative susceptibility mapping via approximate message passing with parameter estimation.

For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion. We propose a probabilistic Bayesian approach for QSM with built-in parameter estimation, and incorporate the nonlinear formulation of the dipole inversion to achieve a robust recovery of the susceptibility maps. From a Bayesian perspective, the image wavelet coefficients are approximately sparse and modeled by the Laplace distribution. The measurement noise is modeled by a Gaussian-mixture distribution with two components, where the second component is used to model the noise outliers. Through probabilistic inference, the susceptibility map and distribution parameters can be jointly recovered using approximate message passing (AMP). We compare our proposed AMP with built-in parameter estimation (AMP-PE) to the state-of-the-art L1-QSM, FANSI, and MEDI approaches on the simulated and in vivo datasets, and perform experiments to explore the optimal settings of AMP-PE. Reproducible code is available at: https://github.com/EmoryCN2L/QSM_AMP_PE. On the simulated Sim2Snr1 dataset, AMP-PE achieved the lowest NRMSE, deviation from calcification moment and the highest SSIM, while MEDI achieved the lowest high-frequency error norm. On the in vivo datasets, AMP-PE is robust and successfully recovers the susceptibility maps using the estimated parameters, whereas L1-QSM, FANSI and MEDI typically require additional visual fine-tuning to select or double-check working parameters. AMP-PE provides automatic and adaptive parameter estimation for QSM and avoids the subjectivity from the visual fine-tuning step, making it an excellent choice for the clinical setting.

Application of scattering image wavelet transform in cave recognition: A case study on a bedrock buried hill reservoir in Bongor Basin, Chad

Application of scattering image wavelet transform in cave recognition: A case study on a bedrock buried hill reservoir in Bongor Basin, Chad