This study presents a noise-resilient masked-face detection framework optimized for the NVIDIA Jetson AGX Orin, which improves detection precision by approximately 30% under severe Gaussian noise (variance 0.10) while reducing denoising latency by ov...
In underwater signal processing, accurate time delay estimation (TDE) is of crucial importance for ensuring the reliability of data transmission. However, the complex propagation of sound waves and strong noise interference in the underwater environm...
Addressing the noise in seismic signals, a prevalent challenge within seismic signal processing, has been the focus of extensive research. Conventional algorithms for seismic signal denoising often fall short due to their reliance on manually determi...
To overcome the interference of noise on the exploration effectiveness of the controlled-source electromagnetic method (CSEM), we improved the deep learning algorithm by combining the denoising convolutional neural network (DnCNN) with the residual n...
Three-dimensional imaging plays a crucial role at the micro-scale in fields such as precision manufacturing and materials science. However, image noise significantly impacts the accuracy of point cloud reconstruction, making image denoising technique...
Digital holography is a very efficient technique for 3D imaging and the characterization of changes at the surfaces of objects. However, during the process of holographic interferometry, the reconstructed phase images suffer from speckle noise. In th...
Medical image denoising is crucial for enhancing the diagnostic accuracy of CT and MRI images. This paper presents a modular hybrid framework that combines multiscale decomposition techniques (Empirical Mode Decomposition, Variational Mode Decomposit...
Convolutional neural networks (CNNs) are a state-of-the-art technique for speech emotion recognition. However, CNNs have mostly been applied to noise-free emotional speech data, and limited evidence is available for their applicability in emotional s...
(1) Background: Due to its imaging principle, OCT generates images laden with significant speckle noise. The quality of OCT images is a crucial factor influencing diagnostic effectiveness, highlighting the importance of OCT image denoising. (2) Metho...
Remote sensing image denoising is of great significance for the subsequent use and research of images. Gaussian noise and salt-and-pepper noise are prevalent noises in images. Contemporary denoising algorithms often exhibit limitations when addressin...
With ongoing growth in the implementation of CCTV networks, miniature sensors, and IoT devices, the quality of captured images in terms of authenticity has become a major security issue. Through advanced editing tools and generative models, the capab...
Domain Name Server (DNS) amplification Distributed Reflection Denial of Service (DRDoS) attacks are a Distributed Denial of Service (DDoS) attack technique in which multiple IT systems forge the original IP of the target system, send a request to the...
Pedestrian detection is an essential task for computer vision and the automotive industry. Complex systems like advanced driver-assistance systems are based on far-infrared data sensors, used to detect pedestrians at nighttime, fog, rain, and direct...
Digital holography is well adapted to measure any modifications related to any objects. The method refers to digital holographic interferometry where the phase change between two states of the object is of interest. However, the phase images are corr...
In recent years, the study of deep learning techniques for underwater acoustic channel estimation has gained widespread attention. However, existing neural network channel estimation methods often overfit to training dataset noise levels, leading to...
Image denoising, a fundamental step in image processing, has been widely studied for several decades. Denoising methods can be classified as internal or external depending on whether they exploit the internal prior or the external noisy-clean image p...
Accurate channel estimation is the fundamental requirement for recovering underwater acoustic orthogonal time–frequency space (OTFS) modulation signals. As the Doppler effect in the underwater acoustic channel is much more severe than that in t...
Image denoising is a challenging research problem that aims to recover noise-free images from those that are contaminated with noise. In this paper, we focus on the denoising of images that are contaminated with additive white Gaussian noise. For thi...
Pulmonary dynamic contrast-enhanced (DCE) MRI is clinically useful for assessing pulmonary perfusion, but its signal-to-noise ratio (SNR) is limited. A self-supervised learning network-based plug-and-play (PnP) denoising model was developed to improv...
In this paper, we explore the problem of direction-of-arrival (DOA) estimation for a non-uniform linear array (NULA) under strong noise. The compressed sensing (CS)-based methods are widely used in NULA DOA estimations. However, these methods commonl...
Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced...
Unlike high-dose scans, low-dose cardiac CT perfusion imaging reduces patient radiation exposure and thereby the risk of potential health effects. However, it introduces significant image noise, degrading diagnostic quality and limiting clinical asse...
Recently, deep learning-based image quality enhancement models have been proposed to improve the perceptual quality of distorted synthesized views impaired by compression and the Depth Image-Based Rendering (DIBR) process in a multi-view video system...
Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel atte...
Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transm...
A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estim...
The objective of this study is to propose an advanced image enhancement strategy to address the challenge of reducing radiation doses in pediatric renal scintigraphy. Data from a public dynamic renal scintigraphy database were used. Based on noisier...
Structural health monitoring (SHM) is crucial for ensuring the safety and longevity of military training aircraft, which face demanding conditions such as high maneuverability, variable loads, and extreme environments, leading to structural fatigue....
Signal-to-Noise Ratio (SNR) is the benchmark to evaluate the quality of optical remote sensors. For SNR estimation, most of the traditional methods have complicated processes, low efficiency, and general accuracy. In particular, they are not suitable...
Data augmentation (DA) is an effective way to enrich the richness of data and improve a model’s generalization ability. It has been widely used in many advanced vision tasks (e.g., classification, recognition, etc.), while it can hardly be seen...
Ground-penetrating radar (GPR) is often used to detect targets in a construction environment. Due to the different construction environments, the noise exhibits different characteristics on the GPR signal. When the noise is widely distributed on the...
Nowadays, reliable medical diagnostics from computed tomography (CT) and X-rays can be obtained by using a large number of image edge detection methods. One technique with a high potential to improve the edge detection of images is ant colony optimiz...
We propose a deep learning architecture, dubbed Plug-and-play 2D ADMM-Net (PAN), by combining model-driven deep networks and data-driven deep networks for effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging with various signa...
Digital images often become corrupted by undesirable noise during the process of acquisition, compression, storage, and transmission. Although the kinds of digital noise are varied, current denoising studies focus on denoising only a single and speci...
Microfluidics is a highly interdisciplinary field where the integration of deep-learning models has the potential to streamline processes and increase precision and reliability. This study investigates the use of deep-learning methods for the accurat...
Attaining precise target detection and channel measurements are critical for guiding beamforming optimization and data demodulation in massive multiple-input multiple-output (MIMO) communication systems with hybrid structures, which requires large pi...
The deep-learning steganography of current hotspots can conceal an image secret message in a cover image of the same size. While the steganography secret message is primarily removed via active steganalysis. The document image as the secret message i...
Under sparse aperture (SA) conditions, inverse synthetic aperture radar (ISAR) imaging becomes a severely ill-posed inverse problem due to undersampled and noisy measurements, leading to pronounced degradation in azimuth resolution and image quality....
Medical imaging is crucial for disease diagnosis, but noise in CT and MRI scans can obscure critical details, making accurate diagnosis challenging. Traditional denoising methods and deep learning techniques often produce overly smooth images that la...
The accurate detection of high-impedance faults (HIFs) in distribution systems is fundamentally dependent on the extraction of weak fault signatures. However, these features are often obscured by complex and high-level noise present in current transf...
X-ray Fluorescence Computed Tomography (XFCT) is an emerging non-invasive imaging technique providing high-resolution molecular-level data. However, increased sensitivity with current benchtop X-ray sources comes at the cost of high radiation exposur...
In OFDM wireless communication systems, low-resolution channel characteristics and noise interference pose significant challenges to accurate channel estimation. To solve these problems, we propose a super-resolution denoising residual network (SDRNe...
Micro-Doppler signatures play a crucial role in capturing target features for the radar classification task, and the time–frequency distribution method is widely used to represent micro-Doppler signatures in many applications including human ac...
While unsupervised denoising models have shown progress in recent years, their noise reduction capabilities still lag behind those of supervised denoising models. This limitation can be attributed to the lack of effective constraints during training,...
The adoption of DNS over HTTPS (DoH) has significantly enhanced user privacy and security by encrypting DNS queries. However, it also presents new challenges for detecting malicious activities, such as DNS tunneling, within encrypted traffic. In this...
Convolutional neural networks (CNN) have been widely adopted in fluid dynamics investigations over the past few years due to their ability to extract and process fluid flow field characteristics. Both in sparse-grid simulations and sensor-based exper...
The integration of renewable resources with distribution networks (DNs) is an effective way to reduce carbon emissions in energy systems. In this paper, an economic and low-carbon-oriented optimal planning solution for the integration of photovoltaic...
Dark skin-type individuals have a greater tendency to have pigmentary disorders, among which melasma is especially refractory to treat and often recurs. Objective measurement of melanin amount helps evaluate the treatment response of pigmentary disor...
DNS over HTTPS (DoH) is an advanced version of the traditional DNS protocol that prevents eavesdropping and man-in-the-middle attacks by encrypting queries and responses. However, it introduces new challenges such as encrypted traffic communication,...
Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark displacement (Dn) is employed as the earthquake-related factor...
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