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The paper presents an improvement of the watermark extraction in speech signal watermarking. The noise added to the speech signal during transmission affects the efficiency of the watermark extraction. Removing this noise may aid to enhance the extraction process. There are methods of the speech signal enhancement, which aims to reduce the noise distortion in the speech signal. In this paper, the watermark process is done using the hybrid strategy of the Empirical Mode Decomposition (EMD) and the block-based Singular Value Decomposition (block-based SVD) with chaotic encrypted watermark. The watermark is embedded into the Singular Values matrix (SVs) because of its stability against any disturbance in the speech signal due to the different attacks. The encrypted watermark increases the security level of the watermark. When the watermark extracted at the receiver side, the speech signal will be enhanced first using spectral subtraction, Wiener filter or adaptive Wiener filter enhancement methods. The paper introduces a comparison study to evaluate the performance of each of them. Simulation results indicate that using the enhancement step improve the watermark extraction, especially using the adaptive Wiener filter.

With the rapid development in the field of artificial intelligence and the advancement of deep learning theory, vehicle target detection technology has been widely used in the field of urban intelligent transportation and automatic driving, assisting vehicles to achieve safe driving in complex driving environments and improving traffic safety. This paper proposes a dynamic fuzzy image processing method based on Wiener filter and generative adversarial network, and constructs a UNIT-based de-fogging and de-raining algorithm, which can be generalized to clarify the targets obtained in rainy and foggy scenes. Then design the local perception enhancement vehicle detection model assisted by image rain removal to realize the accurate detection of vehicle targets in rainy and foggy scenes. By applying the method of this paper on the synthetic dataset Rain Vehicle Color-24, the results demonstrate that the mAP values of this paper’s method are 3.73%, 2.23% and 1.19% higher than those of Da-Faster, SA-Da-Faster and SMNN-MSFF respectively, which are able to improve the vehicle color recognition task in rainy and foggy scenes with good Accuracy. Therefore, the method in this paper can reduce the domain differences of the model in the target domain and improve the localization accuracy.

A new adaptive filter algorithm, the iterative Wiener filter (IWF), is proposed to overcome the drawback of slow convergence speed for most LMS-type algorithms. The adaptive filter is posed as a problem of finding the solution of a linear matrix equation, equivalent to the Wiener equation. Then the step size is optimised, which is time variant in terms of the residual error in each step. This property gives the IWF the ability of fast convergent speed. The stability of the algorithm can be secured when the estimation of covariance and cross-covariance statistics become stationary. Only the product of the matrix and vector is needed for the implementation in each iteration. Numerical results demonstrate the superior performance of the IWF over some other LMS-type algorithms.

Variational Mode Decomposition (VMD) serves as an effective method for simultaneously decomposing signals into a series of narrowband components. However, its theoretical foundation, the classical Wiener filter, exhibits limited adaptability when applied to broadband signals. This paper proposes a novel Variable Filtered-Waveform Variational Mode Decomposition (VFW-VMD) method to address critical limitations in VMD, particularly in handling broadband and chirp signals. By incorporating fractional-order constraints and dynamically adjusting filter waveforms, the proposed algorithm effectively mitigates mode mixing and over-smoothing issues. The mathematical framework of VFW-VMD is formulated, and its decomposition performance is validated through simulations involving both synthetic and real-world signals. The results demonstrate that VFW-VMD exhibits superior adaptability in extracting broadband signals and effectively captures more rolling bearing fault features. This work advances signal processing techniques, enhancing capability and significantly improving the performance of practical bearing fault diagnostic applications.

Terrestrial gravity fluctuations are a target of scientific studies in a variety of fields within geophysics and fundamental-physics experiments involving gravity such as the observation of gravitational waves. In geophysics, these fluctuations are typically considered as signal that carries information about processes such as fault ruptures and atmospheric density perturbations. In fundamental-physics experiments, it appears as environmental noise, which needs to be avoided or mitigated. This article reviews the current state-of-the-art of modeling high-frequency terrestrial gravity fluctuations and of gravity-noise mitigation strategies. It hereby focuses on frequencies above about 50 mHz, which allows us to simplify models of atmospheric gravity perturbations (beyond Brunt–Väisälä regime) and it guarantees as well that gravitational forces on elastic media can be treated as perturbation. Extensive studies have been carried out over the past two decades to model contributions from seismic and atmospheric fields especially by the gravitational-wave community. While terrestrial gravity fluctuations above 50 mHz have not been observed conclusively yet, sensitivity of instruments for geophysical observations and of gravitational-wave detectors is improving, and we can expect first observations in the coming years. The next challenges include the design of gravity-noise mitigation systems to be implemented in current gravitational-wave detectors, and further improvement of models for future gravitational-wave detectors where terrestrial gravity noise will play a more important role. Also, many aspects of the recent proposition to use a new generation of gravity sensors to improve real-time earthquake early-warning systems still require detailed analyses.

A critical step in detection of primary intracerebral haemorrhage (ICH) is an accurate assessment of computed tomography (CT) brain images. The correct diagnosis relies on imaging modality and quality of acquired images. The authors present an enhancement algorithm which can improve the clarity of edges on CT images. About 40 samples of CT brain images with final diagnosis of primary ICH were obtained from the UKM Medical Centre in Digital Imaging and Communication in Medicine format. The images resized from 512 × 512 to 256 × 256 pixel resolution to reduce processing time. This Letter comprises of two main sections; the first is denoising using Wiener filter, non-local means and wavelet; the second section focuses on image enhancement using a modified unsharp masking (UM) algorithm to improve the visualisation of ICH. The combined approach of Wiener filter and modified UM algorithm outperforms other combinations with average values of mean square error, peak signal-to-noise ratio, variance and structural similarity index of 2.89, 31.72, 0.12 and 0.98, respectively. The reliability of proposed algorithm was evaluated by three blinded assessors which achieved a median score of 65%. This approach provides reliable validation for the proposed algorithm which has potential in improving image analysis.

The spatial response of rectangular pulse heterodyne phase-sensitive optical time-domain reflectometry (ϕ-OTDR) to an acoustic event is characterized by a windowing function rather than a point-like sensitivity. This effect degrades the system’s spatial resolution and introduces systematic errors in array signal processing. This work presents modeling analysis and a mitigation strategy for this fundamental limitation. The spatial windowing effect is modeled as a point spread function (PSF) derived from physical mechanisms and system parameters, including the pulse width, gauge length, and intra-pulse intensity dynamics. The PSF model is validated against measurements under near-ideal conditions using a fiber-coupled tuning fork. A Wiener filter-based deconvolution method is utilized to invert the windowed spatial response towards a point-like response. The effectiveness of this inversion is demonstrated through enhanced spatial resolution and accurate reconstruction of two-dimensional wavefront geometry. Furthermore, the impact of this effect on array signal processing is quantitatively evaluated. The results demonstrate that the proposed method effectively suppresses systematic errors in wavefield analysis, and specifically enhances the accuracy and confidence of steered response power—phase transform (SRP-PHAT) spatial spectrum estimation. This study provides a systematic framework for understanding, quantifying, and inverting the spatial response in ϕ-OTDR, enabling accurate and interpretable acoustic field sensing.

4D ultrasound microvascular imaging was demonstrated by applying ultrafast Doppler tomography (UFD-T) to the imaging of brain hemodynamics in rodents. In vivo real-time imaging of the rat brain was performed using ultrasonic plane wave transmissions at very high frame rates (18,000 frames per second). Such ultrafast frame rates allow for highly sensitive and wide-field-of-view 2D Doppler imaging of blood vessels far beyond conventional ultrasonography. Voxel anisotropy (100μm×100μm×500μm) was corrected for by using a tomographic approach, which consisted of ultrafast acquisitions repeated for different imaging plane orientations over multiple cardiac cycles. UFT-D allows for 4D dynamic microvascular imaging of deep-seated vasculature (up to 20mm) with a very high 4D resolution (respectively 100μm×100μm×100μm and 10ms) and high sensitivity to flow in small vessels (>1mm/s) for a whole-brain imaging technique without requiring any contrast agent. 4D ultrasound microvascular imaging in vivo could become a valuable tool for the study of brain hemodynamics, such as cerebral flow autoregulation or vascular remodeling after ischemic stroke recovery, and, more generally, tumor vasculature response to therapeutic treatment. [Display omitted] •We describe a new 4D microvascular imaging technique.•Combination of ultrasound ultrafast Doppler and tomographic reconstruction for 3D imaging•The technique reaches 100μm resolution and is sensitive to very slow blood flow (1mm/s).•4D capabilities during one cardiac cycle•It opens the way to 4D imaging on awake and moving animals.

Image quality in high-resolution digital single-lens reflex (DSLR) systems is degraded by Complementary Metal-Oxide-Semiconductor (CMOS) sensor noise and optical imperfections. Sensor noise becomes pronounced under high-ISO (International Organization for Standardization) settings, while optical aberrations such as blur and chromatic fringing distort the signal. Optical and sensor-level noise are distinct and hard to separate, but prior studies suggest that improving optical fidelity can suppress or mask sensor noise. Upon this understanding, we introduce a framework that utilizes densely interpolated Point Spread Functions (PSFs) to recover high-fidelity images. The process begins by simulating Gaussian-based PSFs as pixel-wise chromatic and spatial distortions derived from real degraded images. These PSFs are then encoded into a latent space to enhance their features and used to generate refined PSFs via similarity-weighted interpolation at each target position. The interpolated PSFs are applied through Wiener filtering, followed by residual correction, to restore images with improved structural fidelity and perceptual quality. We compare our method—based on pixel-wise, physical correction, and densely interpolated PSF at pre-processing—with post-processing networks, including deformable convolutional neural networks (CNNs) that enhance image quality without modeling degradation. Evaluations on DIV2K and RealSR-V3 confirm that our strategy not only enhances structural restoration but also more effectively suppresses sensor-induced artifacts, demonstrating the benefit of explicit physical priors for perceptual fidelity.

Many works in the state of the art are interested in the increase of the camera depth of field (DoF) via the joint optimization of an optical component (typically a phase mask) and a digital processing step with an infinite deconvolution support or a neural network. This can be used either to see sharp objects from a greater distance or to reduce manufacturing costs due to tolerance regarding the sensor position. Here, we study the case of an embedded processing with only one convolution with a finite kernel size. The finite impulse response (FIR) filter coefficients are learned or computed based on a Wiener filter paradigm. It involves an optical model typical of codesigned systems for DoF extension and a scene power spectral density, which is either learned or modeled. We compare different FIR filters and present a method for dimensioning their sizes prior to a joint optimization. We also show that, among the filters compared, the learning approach enables an easy adaptation to a database, but the other approaches are equally robust.