Spatial Domain Image Research Articles

Learning-based motion artifact correction in the Z-spectral domain for chemical exchange saturation transfer MRI.

To develop and evaluate a physics-driven, saturation contrast-aware, deep-learning-based framework for motion artifact correction in CEST MRI. A neural network was designed to correct motion artifacts directly from a Z-spectrum frequency (Ω) domain rather than an image spatial domain. Motion artifacts were simulated by modeling 3D rigid-body motion and readout-related motion during k-space sampling. A saturation-contrast-specific loss function was added to preserve amide proton transfer (APT) contrast, as well as enforce image alignment between motion-corrected and ground-truth images. The proposed neural network was evaluated on simulation data and demonstrated in healthy volunteers and brain tumor patients. The experimental results showed the effectiveness of motion artifact correction in the Z-spectrum frequency domain (MOCOΩ) compared to in the image spatial domain. In addition, a temporal convolution applied to a dynamic saturation image series was able to leverage motion artifacts to improve reconstruction results as a denoising process. The MOCOΩ outperformed existing techniques for motion correction in terms of image quality and computational efficiency. At 3 T, human experiments showed that the root mean squared error (RMSE) of APT images decreased from 4.7% to 2.1% at 1 μT and from 6.2% to 3.5% at 1.5 μT in case of "moderate" motion and from 8.7% to 2.8% at 1 μT and from 12.7% to 4.5% at 1.5 μT in case of "severe" motion, after motion artifact correction. The MOCOΩ could effectively correct motion artifacts in CEST MRI without compromising saturation transfer contrast.

Enhancing Cybersecurity: Detecting Hidden Information in Spatial Domain Images Using Convolutional Neural Networks

Enhancing Cybersecurity: Detecting Hidden Information in Spatial Domain Images Using Convolutional Neural Networks

Data Hiding Scheme for Spatial Domain Images Using Fuzzy Logic and Modulus Operation

Data Hiding Scheme for Spatial Domain Images Using Fuzzy Logic and Modulus Operation

Advancements in Spatial Domain Image Steganography: Techniques, Applications, and Future Outlook

Abstract. Image steganography, a technique for transmitting secret information hidden within images over public networks undetected, serves as a discreet alternative to cryptography in the field of information security. This article explores new steganography techniques based on the Least Significant Bit (LSB) method, widely recognized for its simplicity in embedding secret data by altering the least significant bit of pixels in the spatial domain. The performance of these LSB-based methods is critically assessed using criteria such as Peak Signal-to-Noise Ratio (PSNR), embedding capacity, and histogram analysis. A comprehensive review of recent literature provides a foundation for this evaluation, highlighting advancements and identifying areas for future improvement. Additionally, the article discusses practical applications of LSB-based steganography in healthcare, government operations, and cloud storage, suggesting directions for further research and development in this subtly powerful area of data security.

Steganography in Spatial Domain Images: Using Image Edge to Hide the Secret Data with a Quality Stego Image

Steganography in Spatial Domain Images: Using Image Edge to Hide the Secret Data with a Quality Stego Image

Research on Key Technologies of Spatial Domain Image Steganography and Analysis of Typical Applications

In the digital age, secure storage and communication of information are crucial aspects of everyday life, drawing significant global interest. Steganography serves as a pivotal method for concealing private information within various media forms—such as images, videos, and text—enabling secure information transmission. This paper delineates the distinctions between information hiding and encryption and delves into image steganography. The focus here is on spatial-based image steganography techniques, which are broadly categorized into traditional and deep learning-based methods. Traditional methods rely on modifications to the pixel values of the host image, often resulting in minor but detectable changes if not carefully managed. On the other hand, deep learning-based methods leverage neural networks to learn how to encode data in an image in a way that is much harder to detect. A comprehensive performance analysis of these techniques is provided, complete with comparative tables to aid in clarity and reference. Additionally, this paper aims to guide researchers by charting current trends in the field and suggesting future research directions. By exploring these advanced techniques, the paper contributes to the broader discourse on enhancing the security and efficacy of steganographic practices.

Frequency domain attention network for copper chain defect detection in tobacco cutting machine

AbstractThe detection of defects on the copper chain in the production process of tobacco cutters is crucial for ensuring product quality. Traditional defect detection methods often rely on spatial domain image analysis, which not only has a large computational load but also performs poorly in handling high‐frequency noise and complex backgrounds. To address this issue, this paper proposes a novel neural network model based on frequency domain analysis, called frequency domain attention network. This network first utilizes discrete cosine transform to transform the image from the spatial domain to the frequency domain, effectively reducing computational complexity and improving processing speed. Subsequently, through the innovative frequency domain attention module, the network automatically identifies and enhances key discriminative features in the frequency domain, thereby strengthening the model's ability to identify defects. Finally, the frequency domain attention map, after feature extraction and integration, is inputted into the coupling detection head to achieve high‐precision defect detection. The experimental results show that our method outperforms the SOTA method with an increase of 0.03 in AP and 21 in FPS.

FSNet: A dual-domain network for few-shot image classification

Few-shot learning is a challenging task, that aims to learn and identify novel classes from a limited number of unseen labeled samples. Previous work has focused primarily on extracting features solely in the spatial domain of images. However, the compressed representation in the frequency domain which contains rich pattern information is a powerful tool in the field of signal processing. Combining the frequency and spatial domains to obtain richer information can effectively alleviate the overfitting problem. In this paper, we propose a dual-domain combined model called Frequency Space Net (FSNet), which preprocesses input images simultaneously in both the spatial and frequency domains, extracts spatial and frequency information through two feature extractors, and fuses them to a composite feature for image classification tasks. We start from a different view of frequency analysis, linking conventional average pooling to Discrete Cosine Transformation (DCT). We generalize the compression of the attention mechanism in the frequency domain. Consequently, we propose a novel Frequency Channel Spatial (FCS) attention mechanism. Extensive experiments demonstrate that frequency and spatial information are complementary in few-shot image classification, improving the performance of the model. Our method outperforms state-of-the-art approaches on miniImageNet and CUB.

TMP: Temporal Motion Perception with spatial auxiliary enhancement for moving Infrared dim-small target detection

Infrared dim and small target detection has significant research value and application prospects in both military affairs and civil safety scenes. This topic presents substantial challenges due to the small size of the targets and their low contrast against the background in images. In the past ten years, most detection methodologies have primarily focused on single-frame images, mainly relying on spatial-domain image features to identify dim and small targets, with limited consideration of temporal-domain motion features. However, unlike general objects, the image features of dim-small targets are often easy to be interfered by background, and even seriously lost in capturing stages. These deficiencies can be the primary factors to impact the detection performance of single-frame methods. To promote infrared dim-small target detection, this paper proposes a novel multi-frame pipeline called Temporal Motion Perception with spatial auxiliary enhancement (TMP), which consists of two parallel feature extraction branches. One auxiliary branch is designed to capture traditional image features from spatial domain, and the other is proposed to extract the motion features of targets from temporal domain. In addition, a new Complementary Symmetry Weighting module is specially designed to fulfill the cross-domain fusion of spatio-temporal features, further enhancing feature representation. Experimental results on three public datasets demonstrate the efficiency of our proposed scheme. Compared with existing methods, it could often refresh the state-of-the-art (SOTA) performance on most metrics. Our codes are available at https://github.com/UESTC-nnLab/TMP.

Coal-rock CT image enhancement based on spatial domain image decomposition and adaptive enhancement factor

Coal-rock CT image enhancement based on spatial domain image decomposition and adaptive enhancement factor

Frequency-domain image encryption based on IWT and 3D S-box

Most of the existing spatial domain image encryption techniques suffer from the difficulty of resisting cryptographic attacks. For this reason, this paper proposes a frequency-domain based digital image encryption scheme by combining Integer Wavelet Transform (IWT), three-dimensional S-box and chaotic system. First, the plaintext image is decomposed into different frequency subbands by IWT to map the digital image from spatial domain to frequency domain. Second, the plaintext hash value is selected as the dynamic key, and dynamic chaotic pseudo-random sequences with associations are generated, which are used for the encryption of each module respectively. Then, a three-dimensional S-box is designed to encrypt the information-rich low-frequency information using ‘bit-permutation three-dimensional S-box replace ciphertext interleaved diffusion’, while the high-frequency information is encrypted using a lightweight ‘XOR-row column permutation’ operation. Finally, the secure ciphertext for public channel transmission is obtained by the reconstruction method. The scheme of this paper, the frequency domain transformation is implemented through IWT, which enhances the ability to resist attacks. In addition, the diffusion encryption modules employ the introduction of ciphertext interleaved diffusion and parallel encryption mechanisms, thus the algorithm has the ability to resist plaintext attacks. Theoretical analysis and empirical results show that the algorithm has excellent numerical statistical analysis results, which corroborate that it has good confusion, diffusion and avalanche effects, and is able to resist various common cryptographic attacks. The frequency domain image encryption scheme proposed in this paper is a preferred high-security digital image privacy protection technique, so it has good application prospects.

Dual-domain mean-reverting diffusion model-enhanced temporal compressive coherent diffraction imaging

Temporal compressive coherent diffraction imaging is a lensless imaging technique with the capability to capture fast-moving small objects. However, the accuracy of imaging reconstruction is often hindered by the loss of frequency domain information, a critical factor limiting the quality of the reconstructed images. To improve the quality of these reconstructed images, a method dual-domain mean-reverting diffusion model-enhanced temporal compressive coherent diffraction imaging (DMDTC) has been introduced. DMDTC leverages the mean-reverting diffusion model to acquire prior information in both frequency and spatial domain through sample learning. The frequency domain mean-reverting diffusion model is employed to recover missing information, while hybrid input-output algorithm is carried out to reconstruct the spatial domain image. The spatial domain mean-reverting diffusion model is utilized for denoising and image restoration. DMDTC has demonstrated a significant enhancement in the quality of the reconstructed images. The results indicate that the structural similarity and peak signal-to-noise ratio of images reconstructed by DMDTC surpass those obtained through conventional methods. DMDTC enables high temporal frame rates and high spatial resolution in coherent diffraction imaging.

An efficient multimodal sentiment analysis in social media using hybrid optimal multi-scale residual attention network

Sentiment analysis is a key component of many social media analysis projects. Additionally, prior research has concentrated on a single modality in particular, such as text descriptions for visual information. In contrast to standard image databases, social images frequently connect to one another, making sentiment analysis challenging. The majority of methods now in use consider different images individually, rendering them useless for interrelated images. We proposed a hybrid Arithmetic Optimization Algorithm- Hunger Games Search (AOA-HGS)-optimized Ensemble Multi-scale Residual Attention Network (EMRA-Net) technique in this paper to explore the modal correlations including texts, audio, social links, and video for more effective multimodal sentiment analysis. The hybrid AOA-HGS technique learns complementary and comprehensive features. The EMRA-Net uses two segments, including Ensemble Attention CNN (EA-CNN) and Three-scale Residual Attention Convolutional Neural Network (TRA-CNN), to analyze the multimodal sentiments. The loss of spatial domain image texture features can be reduced by adding the Wavelet transform to TRA-CNN. The feature-level fusion technique known as EA-CNN is used to combine visual, audio, and textual information. The proposed method performs significantly better than the existing multimodel sentimental analysis techniques of HALCB, HDF, and MMLatch when evaluated using the Multimodal Emotion Lines Dataset (MELD) and EmoryNLP datasets. Also, even though the size of the training set varies, the proposed method outperformed other techniques in terms of recall, accuracy, F score, and precision and takes less time to compute in both datasets.

Estimation of spectral similarities utilizing segmented regions' probability distribution in the block-optimized pan-sharpened image for material classification.

Pan-sharpening is an image fusion approach that combines the spectral information in multispectral (MS) images with the spatial properties of PAN (Panchromatic) images. This vital technique is used in categorization, detection, and other remote sensing applications. In the first step, the article focuses on increasing the finer spatial details in the MS image with PAN images using two levels of fusion without causing spectral deterioration. The suggested fusion method efficiently utilizes image transformation techniques and spatial domain image fusion methods. The luminance component of MS images typically contains spatial features that are not as detailed as the PAN images. A multiscale transform is applied to the intensity/luminance component and PAN image to introduce features into the intensity component. In the first level of processing, coefficients obtained from the non-subsampled contourlet transform are subjected to particle swarm optimization weighted block-based fusion. The second level of fusion is carried out using the concept of spatial frequency to reduce spectral distortion. Numerous reference and non-reference parameters are used to evaluate the sharpened image's quality. In the next step, the article focuses on designing an evaluation metric for analysing spectral distortion based on the Bhattacharyya coefficient and distance. The Bhattacharyya coefficient and distance are calculated for each segmented region to assess the sharpened images' quality. Spectral degradation analysis using proposed techniques can also be useful for analysing materials in the segmented regions. The research findings demonstrate that the spatial features of fused images obtained from the proposed technique increased with the least spectral degradation.

Early-Stage Pine Wilt Disease Detection via Multi-Feature Fusion in UAV Imagery

Pine wilt disease (PWD) is a highly contagious and devastating forest disease. The timely detection of pine trees infected with PWD in the early stage is of great significance to effectively control the spread of PWD and protect forest resources. However, in the spatial domain, the features of early-stage PWD are not distinctly evident, leading to numerous missed detections and false positives when directly using spatial-domain images. However, we found that frequency domain information can more clearly express the characteristics of early-stage PWD. In this paper, we propose a detection method based on deep learning for early-stage PWD by comprehensively utilizing the features in the frequency domain and the spatial domain. An attention mechanism is introduced to further enhance the frequency domain features. Employing two deformable convolutions to fuse the features in both domains, we aim to fully capture semantic and spatial information. To substantiate the proposed method, this study employs UAVs to capture images of early-stage pine trees infected with PWD at Dahuofang Experimental Forest in Fushun, Liaoning Province. A dataset of early infected pine trees affected by PWD is curated to facilitate future research on the detection of early-stage infestations in pine trees. The results on the early-stage PWD dataset indicate that, compared to Faster R-CNN, DETR and YOLOv5, the best-performing method improves the average precision (AP) by 17.7%, 6.2% and 6.0%, and the F1 scores by 14.6%, 3.9% and 5.0%, respectively. The study provides technical support for early-stage PWD tree counting and localization in the field in forest areas and lays the foundation for the early control of pine wood nematode disease.

CVTStego-Net: A convolutional vision transformer architecture for spatial image steganalysis

The principal investigations in image steganalysis in the spatial domain have concentrated on convolutional neural network (CNN) designs. However, existing CNNs increase the local receptive field of steganographic noise without considering global steganographic noise. This study introduces CVTStego-Net, a convolutional vision transformer for spatial domain image steganalysis that merges the strengths of convolutions and the advantages of attention mechanisms to capture both local and global dependencies. CVTStego-Net is composed of three stages: preprocessing stage, noise extraction, and analysis stage, and classification stage. The preprocessing stage involves a bifurcation with trainable and untrainable 30 SRM (Spatial Rich Models) filters to enhance steganographic noise. The noise extraction and analysis stage combines the SE-Block (Squeeze-and-Excitation) with residual operations to increase the sensitivity to steganographic noise and suppressing the influence of redundant information, and the classification stage combines SE-Block with a convolutional vision transformer to connect the local and global spatial relationships of the steganographic noise. This work enhanced the classification accuracies for steganographic algorithms compared to YEDROUDJ-Net, SR-Net, ZHU-Net, GBRAS-Net, and SNMC-Net. Specifically, the accuracy of CVTStego-Net for WOW at 0.2 bpp was 86.58%, and 0.4 bpp was 93.80%. Moreover, for S-UNIWARD at 0.2 and 0.4 bpp, the accuracies were 80.70% and 90.45%, respectively. For MiPOD at 0.2 and 0.4 bpp, the accuracies were 74.70% and 81.48%, respectively. For HILL at 0.2 and 0.4 bpp, the accuracies were 76.70% and 85.80%, respectively, and for HUGO at 0.2 and 0.4 bpp, the accuracies were 78.20% and 86.98%, respectively, using test data from the BOSSbase 1.01. The results demonstrate that convolutional vision transformers can classify steganographic images in the spatial domain.

Employing combined spatial and frequency domain image features for machine learning-based malware detection

<p>The ubiquitous adoption of Android devices has unfortunately brought a surge in malware threats, compromising user data, privacy concerns, and financial and device integrity, to name a few. To combat this, numerous efforts have explored automated botnet detection mechanisms, with anomaly-based approaches leveraging machine learning (ML) gaining attraction due to their signature-agnostic nature. However, the problem lies in devising accurate ML models which capture the ever evolving landscape of malwares by effectively leveraging all the possible features from Android application packages (APKs).This paper delved into this domain by proposing, implementing, and evaluating an image-based Android malware detection (AMD) framework that harnessed the power of feature hybridization. The core idea of this framework was the conversion of text-based data extracted from Android APKs into grayscale images. The novelty aspect of this work lied in the unique image feature extraction strategies and their subsequent hybridization to achieve accurate malware classification using ML models. More specifically, four distinct feature extraction methodologies, namely, Texture and histogram of oriented gradients (HOG) from spatial domain, and discrete wavelet transform (DWT) and Gabor from the frequency domain were employed to hybridize the features for improved malware identification. To this end, three image-based datasets, namely, Dex, Manifest, and Composite, derived from the information security centre of excellence (ISCX) Android Malware dataset, were leveraged to evaluate the optimal data source for botnet classification. Popular ML classifiers, including naive Bayes (NB), multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were employed for the classification task. The experimental results demonstrated the efficacy of the proposed framework, achieving a peak classification accuracy of 93.03% and recall of 97.1% for the RF classifier using the Manifest dataset and a combination of Texture and HOG features. These findings validate the proof-of-concept and provide valuable insights for researchers exploring ML/deep learning (DL) approaches in the domain of AMD.</p>

A light-weight skeleton human action recognition model with knowledge distillation for edge intelligent surveillance applications

Skeleton based human action recognition has evolved as one of the most important applications in multimedia IoT system. However, it requires extensive computation resource including high performance computing unites and large memory to train a deep mode with large number of parameters, which seriously limits it effectiveness and efficiency for edge intelligence multimedia IoT applications. In this paper, a knowledge distillation based light-weight deep model is proposed for skeleton human action recognition to meet the edge multimedia IoT applications. It can get competitive recognition performance in terms of learning accuracy for combination of AI model and edge surveillance equipment. On the one hand, to achieve desirable accuracy, we propose a deep pose-transition image representation method based on two-stream spatial–temporal architecture, which can mine the hidden features of color texture images in spatial and temporal domain, and fuse them for comprehensive discrimination before final classification. On the other hand, to increase the transfer learning ability to the student model on the edge device, we use tucker decomposition to weak the teacher model during knowledge transfer learning process. Finally, in order to validate the effectiveness of our proposal, we conducted extensive experiments to evaluate the proposed approach. The experimental results demonstrate that our proposal can realize deep model miniaturization to meet the requirement of edge multimedia IoT system and achieve the competitive performance.

A multi-level wavelet-based underwater image enhancement network with color compensation prior

Due to the scattering of light and the influence of different water types, underwater images usually suffer from different type of hybrid degradation, e.g. color distortion, blurred details and low contrast. Existing underwater image enhancement methods are weak at handling hybrid degradation simultaneously, resulting in low quality results. Inspired by the fact that wavelet-based enhancement methods can correct color and enhance details in frequency domain and the color compensation prior can compensate missing color information in spatial domain, we design the Multi-level Wavelet-based Underwater Image Enhancement Network (MWEN) with the color compensation prior to enhance image in both frequency domain and spatial domain. Specifically, we integrate the multi-level wavelet transform and the color compensation prior into a multi-stage enhancement framework, where each stage consists of a Multi-level Wavelet-based Enhancement Module (MWEM), a Color Compensation Prior Extraction Module (CCPEM) and a color filter with prior-aware weights. The MWEM decomposes image features into low frequency and high frequency by a wavelet transform, and then enhances them by a low frequency enhancement branch and several high frequency enhancement branches, respectively. The low frequency reduces the color distortion of different water types using Instance Normalization for style transfer, while the high frequency enhancement enhances sparse details using a non-local sparse attention mechanism. After the inverse wavelet transform, the preliminary enhanced result by the MWEM is obtained. Then, the color filter whose weights are customized by the color compensation information extracted from the CCPEM dynamically is applied to output of the MWEM for color compensation. Such an operation enables network to adapt to hybrid degradation and achieve better performance. The experiments demonstrate MWEN outperforms existing UIE methods quantitatively and qualitatively.

Improving Reconstruction of Structured Illumination Microscopy Images via Dual-Domain Learning

Structured illumination microscopy (SIM) provides an enhanced resolving power surpassing the optical diffraction limit by optical modulation of patterned illuminations. Although end-to-end deep learning techniques have recently advanced the reconstruction of SIM images, the reconstruction fidelity of existing networks is still moderate. We experimentally point out the crux lies in the inability of these models for faithful frequency learning. As a remedy, we propose a dual-domain learning strategy for SIM reconstruction, namely DDL-SIM, which learns to reconstruct SIM images from raw images in the spatial domain and raw image spectra in the frequency domain simultaneously, with the goal of narrowing the reconstruction gaps in both domains, thereby better recovering modulated frequencies and resolving more fine structures. Reconstruction experiments across various biological structures demonstrate the proposed DDL-SIM significantly improves the reconstruction fidelity of SIM images and shows great robustness against reconstruction artifacts.