Search Results (64)

To solve the problem of the limited capability of multimodal image feature descriptors constructed by gradient information and the phase consistency principle, a method of cumulative structure feature descriptor construction with rotation invariance is proposed in this paper. Firstly, we extract the direction of multi-scale and multi-direction feature point edges using the Log-Gabor odd-symmetric filter and calculate the amplitude of pixel edges based on the phase consistency principle. Then, the main direction of the key points is determined based on the edge direction feature map, and the coordinates are established according to the main direction to ensure that the feature point descriptor has rotation invariance. Finally, the Log-Gabor odd-symmetric filter calculates the cumulative structural response in the maximum direction and constructs a highly identifiable descriptor with rotation invariance. We select several representative heterogeneous images as test data and compare the matching performance of the proposed algorithm with several excellent descriptors. The results indicate that the descriptor constructed in this paper is more robust than other descriptors for heterosource images with rotation changes. Full article

Variations in terrain elevation cause images acquired under different imaging modalities to deviate from a linear mapping relationship. This effect is particularly pronounced between optical and SAR images, where the range-based imaging mechanism of SAR sensors leads to significant local geometric distortions, such as perspective shrinkage and occlusion. As a result, it becomes difficult to represent the spatial correspondence between optical and SAR images using a single geometric model. To address this challenge, we propose a global optical-SAR image registration method that leverages local distortion characteristics. Specifically, we introduce a Superpixel-based Local Distortion Division (SLDD) method, which defines superpixel region features and segments the image into local distortion and normal regions by computing the Mahalanobis distance between superpixel features. We further design a Multi-Feature Fusion Capsule Network (MFFCN) that integrates shallow salient features with deep structural details, reconstructing the dimensions of digital capsules to generate feature descriptors encompassing texture, phase, structure, and amplitude information. This design effectively mitigates the information loss and feature degradation problems caused by pooling operations in conventional convolutional neural networks (CNNs). Additionally, a hard negative mining loss is incorporated to further enhance feature discriminability. Feature descriptors are extracted separately from regions with different distortion levels, and corresponding transformation models are built for local registration. Finally, the local registration results are fused to generate a globally aligned image. Experimental results on public datasets demonstrate that the proposed method achieves superior performance over state-of-the-art (SOTA) approaches in terms of Root Mean Squared Error (RMSE), Correct Match Number (CMN), Distribution of Matched Points (Scat), Edge Fidelity (EF), and overall visual quality. Full article

The periodic rotation of the Ocean Color and Temperature Scanner (OCTS) introduces jitter errors in the HaiYang-3A (HY-3A) satellite, leading to internal geometric distortion in optical imagery and significant registration errors in multispectral images. These issues severely influence the application value of the optical data. To achieve near real-time compensation, a novel jitter error estimation and correction method based on multi-source attitude data fusion is proposed in this paper. By fusing the measurement data from star sensors and gyroscopes, satellite attitude parameters containing jitter errors are precisely resolved. The jitter component of the attitude parameter is extracted using the fitting method with the optimal sliding window. Then, the jitter error model is established using the least square solution and spectral characteristics. Subsequently, using the imaging geometric model and stable resampling, the optical remote sensing image with jitter distortion is corrected. Experimental results reveal a jitter frequency of 0.187 Hz, matching the OCTS rotation period, with yaw, roll, and pitch amplitudes quantified as 0.905”, 0.468”, and 1.668”, respectively. The registration accuracy of the multispectral images from the Coastal Zone Imager improved from 0.568 to 0.350 pixels. The time complexity is low with the single-layer linear traversal structure. The proposed method can achieve on-orbit near real-time processing and provide accurate attitude parameters for on-orbit geometric processing of optical satellite image data. Full article

In recent years, synthetic aperture radar (SAR) technology has been increasingly explored for automotive applications. However, automotive SAR images generated via matched filter (MF) often exhibit challenges such as noisy backgrounds, sidelobe artifacts, and limited resolution. Sparse regularization methods have the potential to enhance image quality. Nevertheless, conventional unweighted ${\mathcal{l}}_1$ regularization methods struggle to address cases with radar cross section (RCS) distributed over a wide dynamic range, often resulting in insufficient sidelobe suppression, amplitude distortion, and inconsistent super-resolution performance. In this paper, we propose a novel reweighted regularization method, termed multi-segment-reweighted regularization (MSR), for automotive SAR image restoration. By introducing a novel weighting scheme, MSR localizes the global scattering point enhancement problem to the mainlobe scale, effectively mitigating sidelobe interference. This localization ensures consistent enhancement capability independent of RCS variations. Furthermore, MSR employs multi-segment regularization to constrain amplitude within the mainlobes, preserving the characteristics of the original response. Correspondingly, a new thresholding function, named Thinner Response Undistorted THresholding (TRUTH), is introduced. An iterative algorithm for enhancing automotive SAR images using MSR is also presented. Real data experiments validate the feasibility and effectiveness of the proposed method. Full article

During image collection, images are often polluted by noise because of imaging conditions and equipment limitations. Images are also disturbed by external noise during compression and transmission, which adversely affects consequent processing, like image segmentation, target recognition, and text detection. A two-dimensional amplitude image is one of the most common image categories, which is widely used in people’s daily life and work. Research on this kind of image-denoising algorithm is a hotspot in the field of image denoising. Conventional denoising methods mainly use the nonlocal self-similarity of images and sparser representatives in the converted domain for image denoising. In particular, the three-dimensional block matching filtering (BM3D) algorithm not only effectively removes the image noise but also better retains the detailed information in the image. As artificial intelligence develops, the deep learning-based image-denoising method has become an important research direction. This review provides a general overview and comparison of traditional image-denoising methods and deep neural network-based image-denoising methods. First, the essential framework of classic traditional denoising and deep neural network denoising approaches is presented, and the denoising approaches are classified and summarized. Then, existing denoising methods are compared with quantitative and qualitative analyses on a public denoising dataset. Finally, we point out some potential challenges and directions for future research in the field of image denoising. This review can help researchers clearly understand the differences between various image-denoising algorithms, which not only helps them to choose suitable algorithms or improve and innovate on this basis but also provides research ideas and directions for subsequent research in this field. Full article

Conducting vibration monitoring during bridge construction is of significance for ensuring the safety of personnel and property and achieving safety risk management and controlling. However, current bridge vibration monitoring faces numerous challenges, including a large number of measurement points, significant frequency differences, vast structural scales, lack of fixed reference points, and difficulties in temporary deployment. This paper proposes a method for bridge structural vibration monitoring based on computer vision. The method utilizes high-definition cameras to capture dynamic images of bridges and incorporates advanced image processing algorithms to automatically identify and track the vibration characteristics of bridge structures, achieving low energy consumption, low cost, and high efficiency in monitoring. For developing this method, experiments were first conducted in an indoor environment using preset templates, where the amplitude error was within 0.5% and the frequency error was within 0.2%, verifying the feasibility and accuracy of the method. Subsequently, the size of the templates was altered, and the experimental results for five different template sizes were compared. The frequency errors were all within 0.2%, and the amplitude errors were all within 0.5%, with minimal differences, demonstrating the adaptability of the method. Subsequently, under the same indoor conditions, monitoring is conducted using the feature-based template matching method and cross-correlation-based method, respectively. The largest amplitude errors measured by the two methods were 5.59% and 14.39%, respectively, while the frequency errors were 1.82% and 1.02%, respectively. Finally, the method was applied to monitor the displacement of the piers during the jacking construction process of the Yongning Bridge. Full article

To achieve precise detection of target geometric features, Ka/W/sub-terahertz band imaging radar systems with ultra-wide instantaneous bandwidth have been developed. Although dechirp-based receiver architectures allow for low-sampling-rate signal acquisition, they require precise linearity in chirp signals, often necessitating precompensation for nonlinear errors. While most research addresses polynomial-based error correction, periodic errors remain underexplored, despite their potential to obscure weak targets and introduce spurious ones. This paper proposes a novel software-based correction method that integrates neural networks and joint optimization strategies to correct periodic phase errors. The method first employs neural networks for frequency estimation, followed by phase-matching techniques to extract amplitude and phase data. Parameter estimation is refined using the Adaptive Moment Estimation (ADAM) algorithm and Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization. Nonlinear errors are corrected via matched Fourier transforms. Simulations and experiments demonstrate that the proposed method effectively suppresses spurious targets and enhances the detection of weak targets, demonstrating strong robustness and practical applicability, thereby significantly enhancing the target detection performance of the ultra-wideband radar system. Full article

The GF-7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are crucial for achieving high-quality imaging and precise attitude measurements. However, the satellite’s operation is affected by both internal and external factors, which induce vibrations in the satellite platform, thereby affecting image quality and mapping accuracy. To address this challenge, this paper proposes a novel method for constructing a satellite platform vibration model based on geographic location information. The model is developed by integrating composite data from star sensors and gyroscopes (gyro) with subsatellite point location data. The experimental methodology involves the composite processing of gyro data and star sensor optical axis angles, integration of the processed data through time-matching and normalization, and denoising of the integrated data, followed by trigonometric fitting to capture the periodic characteristics of platform vibrations. The positions of the satellite substellar points are determined from the satellite orbit data. A rigorous geometric imaging model is then used to construct a vibration model with geographic location correlation in combination with the satellite subsatellite point positions. The experimental results demonstrate the following: (1) Over the same temporal range, there is a significant convergence in the waveform similarities between the gyro data and the star sensor optical axis angles, indicating a strong correlation in the jitter information; (2) The platform vibration exhibits a robust correlation with the satellite’s geographic location along its orbit. Specifically, the model reveals that the GF-7 satellite experiences the maximum vibration amplitude between 5° S and 20° S latitude during its ascending phase, and the minimum vibration amplitude between 5° N and 20° N latitude during the descending phase. The model established in this study offers theoretical support for optimizing satellite attitude and mitigating platform vibrations. Full article

Background: Spinal cord epidural stimulation (SCES) has the potential to restore motor functions following spinal cord injury (SCI). Spinal cord mapping is a cornerstone step towards successfully configuring SCES to improve motor function, aiming to restore standing and stepping abilities in individuals with SCI. While some centers have advocated for the use of intraoperative mapping to anatomically target the spinal cord locomotor centers, this is a resource-intensive endeavor and may not be a feasible approach in all centers. Methods: Two participants underwent percutaneous SCES implantation as part of a clinical trial. Each participant underwent a temporary (1-week, two-lead) trial followed by a permanent, two-lead implantation. SCES configurations were matched between temporary and permanent mappings, and motor evoked potential in response to 2 Hz, for a duration of 250–1000 µs and with an amplitude of 1–14 mA, was measured using electromyography. T2 axial MRI images captured prior to implantation were used to retrospectively reconstruct the lumbosacral segments of the spinal cord. The effects of lead migration on mapping were further determined in one of the participants. Results: In both participants, there were recognized discrepancies in the recruitment curves of the motor evoked potentials across different muscle groups between temporary and permanent SCES mappings. These may be explained by retrospective MRI reconstruction of the spinal cord, which indicated that the percutaneous leads did not specifically target the entire L1-S2 segments in both participants. Minor lead migration appeared to have a minimal impact on spinal cord mapping outcomes in one of the participants but did dampen the motor activity of the hip and knee muscle groups. Conclusions: Temporary mapping coupled with MRI reconstruction has the potential to be considered as guidance for permanent implantation considering target activation of the spinal cord locomotor centers. Since lead migration may alter the synergistic coordination between different muscle groups and since lead migration of 1–2 contacts is expected and planned for in clinical practice, it can be better guided with proper spinal cord mapping and a diligent SCES lead trial beforehand. Full article

(1) Background: This study investigates the resting-state brain characteristics of skeleton athletes compared to healthy age-matched non-athletes, using resting-state fMRI to investigate long-term skeleton-training-related changes in the brain. (2) Methods: Eleven skeleton athletes and twenty-three matched novices with no prior experience with skeleton were recruited. Amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity analyses were explored to investigate resting-state functional magnetic resonance imaging (rs-fMRI) data, aiming to elucidate differences in resting-state brain function between the two groups. (3) Results: Compared to the control group, skeleton athletes exhibited significantly higher ALFF in the left fusiform, left inferior temporal gyrus, right inferior frontal gyrus, left middle temporal gyrus, left and right insula, left Rolandic operculum, left inferior frontal gyrus, and left superior temporal gyrus. Skeleton athletes exhibit stronger functional connectivity in brain regions associated with cognitive and motor control (superior frontal gyrus, insula), as well as those related to reward learning (putamen), visual processing (precuneus), spatial cognition (inferior parietal), and emotional processing (amygdala), during resting-state brain function. (4) Conclusions: The study contributes to understanding how motor training history shapes skeleton athletes’ brains, which have distinct neural characteristics compared to the control population, indicating potential adaptations in brain function related to their specialized training and expertise in the sport. Full article

The spectral response of an acousto-optic tunable filter (AOTF) is crucial for an AOTF based spectral imaging system. The acousto-optic (AO) interaction within the spatial-distributed area of the acoustic field determines the spectral response of the light incidence. Assuming an ideally uniform acoustic field distribution, phase-matching geometries can be applied to calculate the anisotropic Bragg diffraction in AO interactions, determining the wavelength and direction of the diffracted light. In this ideal scenario, the wavelength of the diffracted light depends solely on the direction of the incident light. However, due to the non-ideal nature of the acoustic field, the wavelength of the diffracted light exhibits slight variations with incident position. In this paper, an analytical model is proposed to calculate the spatial-dependent spectral response of the diffracted light under non-uniform acoustic field distribution. The study computes the variation pattern of the diffracted light amplitude caused by the inhomogeneous acoustic distribution. The theoretical considerations and computational model are confirmed by AOTF frequency scanning experiments. The study demonstrates that the distribution of the acoustic field leads to non-uniform spatial-spectral response in the AOTF, and the spatial AO interaction computational model can provide data support for calibrating AOTF systems in imaging applications. Full article

The motion of near-wall bubble-in-chain, which is a crucial aspect of the study of near-wall bubble flows, involves not only the wall effect but also the interactions between bubbles. However, there have been few studies on this topic. In this study, we investigated the motion of near-wall bubble-in-chain using a dual-camera orthogonal shadow method and tracked bubbles using image processing and feature matching techniques. Considering both the wall effect and bubble generation frequency, we discussed the statistical characteristics, motion modes, dynamic characteristics, and energy evolution of bubbles. The results demonstrate that an increase in bubble generation frequency leads to a greater deviation of bubble trajectories from the wall and an increase in trajectory amplitude while weakening the suppression of bubble speed by the wall. Furthermore, changes in both bubble equivalent diameter and drag coefficient reveal how bubble generation frequency affects their shape stability during motion as well as regulation by the wall effect. The drag coefficient decreases with increasing Reynolds number for bubbles; however, an increase in bubble generation frequency broadens its distribution range. Additionally, it is evident that the wall effect significantly impacts drag characteristics for bubbles: uncollided bubbles experience increased drag coefficients with greater distance from the wall while collided bubbles exhibit decreased drag coefficients. In cases of high generation frequency, the conversion of kinetic energy to surface energy during bubble collisions, especially the enhancement of the peak of surface energy, indicates an increase in the bubble’s energy storage capacity and energy conversion efficiency. The findings not only enhance comprehension of behavior exhibited by near-wall bubbles but also offer a novel perspective for regulating near-wall bubble flows in industrial applications. Full article

Background: The investigation of C-fiber-evoked ultralow-level responses (ULEPs) at somatic sites is difficult in clinical practice but may be useful in patients with small fiber neuropathy. Aim: The aim of the study was to investigate changes in LEPs and ULEPs in patients with fibromyalgia affected or not by abnormal intraepidermal innervation. Methods: We recorded LEPs and ULEPs of the hand, thigh and foot in 13 FM patients with a normal skin biopsy (NFM), 13 patients with a reduced intraepidermal nerve fiber density (IENFD) (AFM) and 13 age-matched controls. We used a YAP laser, changing the energy and spot size at the pain threshold for LEPs and at the heat threshold for ULEPs. Results: ULEPs occurred at a small number of sites in both the NFM and AFM groups compared to control subjects. The absence of ULEPs during foot stimulation was characteristic of AFM patients. The amplitude of LEPs and ULEPs was reduced in patients with AFM at the three stimulation sites, and a slight reduction was also observed in the NFM group. Conclusions: The present preliminary results confirmed the reliability of LEPs in detecting small fiber impairments. The complete absence of ULEPs in the upper and lower limbs, including the distal areas, could confirm the results of LEPs in patients with small fiber impairments. Further prospective studies in larger case series could confirm the present findings on the sensitivity of LEP amplitude and ULEP imaging in detecting small fiber impairments and the development of IENFD in FM patients. Full article

Aiming at the nonlinear radiometric differences between multi-source sensor images and coherent spot noise and other factors that lead to alignment difficulties, the registration method of gradient weakly sensitive multi-source sensor images is proposed, which does not need to extract the image gradient in the whole process and has rotational invariance. In the feature point detection stage, the maximum moment map is obtained by using the phase consistency transform to replace the gradient edge map for chunked Harris feature point detection, thus increasing the number of repeated feature points in the heterogeneous image. To have rotational invariance of the subsequent descriptors, a method to determine the main phase angle is proposed. The phase angle of the region near the feature point is counted, and the parabolic interpolation method is used to estimate the more accurate main phase angle under the determined interval. In the feature description stage, the Log-Gabor convolution sequence is used to construct the index map with the maximum phase amplitude, the heterogeneous image is converted to an isomorphic image, and the isomorphic image of the region around the feature point is rotated by using the main phase angle, which is in turn used to construct the feature vector with the feature point as the center by the quadratic interpolation method. In the feature matching stage, feature matching is performed by using the sum of squares of Euclidean distances as a similarity metric. Finally, after qualitative and quantitative experiments of six groups of five pairs of different multi-source sensor image alignment correct matching rates, root mean square errors, and the number of correctly matched points statistics, this algorithm is verified to have the advantage of robust accuracy compared with the current algorithms. Full article

Within the rapidly growing urban tourism industry, the development of urban landscapes plays a crucial role in shaping a city’s image and competitiveness; however, standardized and mismatched landscapes often have a negative impact, highlighting the importance of assessing urban landscape perceptions. Although existing studies have discussed this through subjective questionnaires and physiological methods, the underlying neural mechanisms have not been thoroughly explored. The research focuses on Jingdezhen, a renowned historical and cultural city in China, as its case study. Utilized the event-related potential (ERP) method to explore individuals’ perceptual consistency and neural activity toward different types of urban landscapes. We adopted a 2 (landscape type: historical, modern) X 2 (perceptual match: consistent, inconsistent) within-subject design while recording behavioral data and electrophysiological responses. The results showed that, under any condition, there were no significant differences in people’s behavioral data. Neurophysiological results indicate that consistent perceptions of modern landscapes elicited greater P200 responses, suggesting increased attention driven by visual aesthetics and emotional activation. Under conditions of perceptual inconsistency, historical landscapes elicited higher N400 amplitudes than modern landscapes, revealing cognitive conflict and effort. This study demonstrates that P200 and N400 components are effective indicators for assessing urban perception, proving the viability of the event-related potential method in urban landscape research. Additionally, the research reveals the neural mechanisms of urban environmental perception from the early stages of attention and emotional distribution to the later stages of cognitive decision-making, which involve cognitive processes from “bottom-up” to “top-down”. This study not only provides a reference for efficient design planning for those involved in urban science but also inspires the coordination between the developmental needs of historical and modern urban landscapes. Moreover, it offers a new perspective for an interdisciplinary approach to urban perception assessment. Full article