Search Results (188)

Synthetic Aperture Radar (SAR) imagery is inherently degraded by multiplicative speckle noise, rendering traditional superpixel methods—which rely on hard assignment and uniform initialization—suboptimal for boundary preservation. This study proposes a complexity-aware superpixel generation framework featuring differentiable soft-assignment optimization. The approach employs an F-LGRP (Fusion of Local Gradient Pattern Representation) feature descriptor that fuses regional gradient statistics via Gaussian filtering to suppress speckle, coupled with a complexity-driven recursive quadtree initialization strategy yielding non-uniform seed density. A U-Net architecture predicts soft pixel–superpixel association maps within a 9-neighborhood constraint, supervised by a multi-objective loss integrating edge information reconstruction and boundary feature reconstruction. Comprehensive evaluations on simulated and real SAR images (WHU-OPT-SAR and Munich) demonstrate that the proposed method achieves state-of-the-art performance across Boundary Recall, Undersegmentation Error, Compactness, and Achievable Segmentation Accuracy compared to SLIC, SNIC, Mean-Shift, PILS, and SSN. Validation on downstream segmentation tasks further confirms superior accuracy and computational efficiency, establishing the framework as an effective solution for end-to-end SAR image analysis. Full article

Seasonal tropical wetlands such as the Brazilian Pantanal are increasingly threatened by climate variability and extreme hydrological events, creating a need for robust monitoring tools that capture flood dynamics at high spatial and temporal resolution. This study used Sentinel-1 Synthetic Aperture Radar (SAR) imagery to map and monitor flooding in the northern Pantanal, a Ramsar site renowned for its wildlife, between 2017 and 2020. Ground Range Detected (GRD) VV-polarized scenes were preprocessed using radiometric terrain normalization and speckle filtering (Lee filter, 5 × 5 window) to improve the separability of water and non-water surfaces. Flooded areas were initially extracted with Otsu’s histogram thresholding and validated using high-resolution optical imagery (PlanetScope and Landsat-8). A supervised Random Forest classifier then refined land-cover discrimination into three classes (open water/flood, open land/vegetation, and others), achieving an overall accuracy of 97.70% on the independent testing dataset (n = 6622), while temporal consistency was supported by Cuiabá River hydrological data. The results revealed strong interannual variability in flood extent, with inundation covering 34.7% of the reserve in March 2017 compared with 0.75% in March 2020 and reaching a peak of 79.9% in April 2017. Overall, Sentinel-1 SAR effectively delineated open water and flood-affected surfaces under persistent cloud cover, demonstrating its value for complementing existing products such as MapBiomas, strengthening wetland management, and supporting scalable flood monitoring in other tropical flood-prone Ramsar sites. Full article

Objective: This prospective study investigated the use of H-scan ultrasound (US) imaging as a novel component of a multiparametric radiomic analysis framework for characterizing human breast cancer response to neoadjuvant chemotherapy (NAC) before and early after treatment initiation. Methods: Thirty breast cancer patients scheduled for NAC were scanned using a clinical US system (Logiq E9, GE HealthCare) equipped with a 9L-D linear array transducer. Radiofrequency (RF) data was obtained at baseline (pre-NAC) and after 10% and 30% of the complete dose of chemotherapy. The RF data was analyzed by a bank of 256 frequency-shifted bandpass filters to form H-scan US frequency images. Grayscale texture features were extracted from both B-scan and H-scan US images. In addition, US attenuation coefficient and speckle statistics based on the Nakagami and Burr distributions were estimated from the RF data. Data classification of tumor and peri-tumoral regions was performed using a novel three-dimensional (3D) score map based on support vector machine (SVM) modeling. Unlike conventional classifiers that report only a single prediction score, a 3D score map provides a visual representation of the classifier decision space, enabling interpretation of class separation and treatment-induced shifts in multiparametric US measurements. Results: The dataset was split into 10 disjoint partitions (90% training, 10% testing) to compute area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy measures. Actual patient response to NAC was assessed at surgery and categorized as either pathologic complete response (pCR) or non-pCR. Multiparametric US and data classification results at pre-NAC found AUC values of 0.78 after using only tumor information (p < 0.01), which increased to 0.81 with inclusion of peri-tumoral information (p < 0.01). Significant differences in multiparametric US measures from both cancer response types was found after integration of patient data collected at 10% completion of the NAC regimen (i.e., first NAC cycle), yielding an improved AUC of 0.86 (p < 0.001). Conclusions: Multiparametric US imaging with radiomic features from both the tumor and peri-tumoral regions is a promising noninvasive approach for monitoring early breast cancer response to NAC. Full article

Side-scan sonar (SSS) serves as the primary perceptual instrument for Autonomous Underwater Vehicles (AUVs) in large-scale marine search and rescue (SAR) operations. However, the detection of critical targets is frequently hindered by severe hydro-acoustic noise, the spatial discontinuity of wreckage, and the weak visual signatures of small targets. To surmount these challenges, this paper presents WPG-DetNet. First, we introduce a Wavelet-Embedded Residual Backbone (WERB) to reconstruct the conventional downsampling paradigm. By substituting standard pooling with the Discrete Wavelet Transform (DWT), this architecture explicitly disentangles high-frequency noise from structural information in the frequency domain, thereby achieving the adaptive preservation of edge fidelity for large human-made targets while filtering out speckle interference. Then, addressing the distinct challenge of discontinuous aircraft wreckage, the framework further incorporates a Debris Graph Reasoning Module (D-GRM). This module models scattered fragments as nodes in a topological graph to capture long-range semantic dependencies, transforming isolated instance recognition into context-aware scene understanding. Finally, to bridge the gap between AI and underwater physics, we design a Shadow-Aided Decoupling Head (SADH) equipped with a physics-informed geometric loss. By enforcing mathematical consistency between target height and acoustic shadow length, this mechanism establishes a rigorous discriminative criterion capable of distinguishing weak-echo human bodies from seabed rocks based on shadow geometry. Experiments on the SCTD dataset demonstrate that WPG-DetNet achieves a mean Average Precision ($mA{P}_{50}$) of 97.5% and a Recall of 96.9%. Quantitative analysis reveals that our framework outperforms the classic Faster R-CNN by a margin of 12.8% in $mA{P}_{50}$ and surpasses the Transformer-based RT-DETR-R18 by 5.6% in high-precision localization metrics ($mA{P}_{50:95}$). Simultaneously, WPG-DetNet maintains superior efficiency with an inference speed of 62.5 FPS and a lightweight parameter count of 16.8 M, striking an optimal balance between robust perception and the real-time constraints of AUV operations. Full article

Detecting ships using Synthetic Aperture Radar (SAR) in coastal areas is still difficult due to the impact of coherent speckle noise from the ocean surface, complex land clutter and having multi-scale target representations in the radar imagery. Most of the existing ship detection algorithms lose important target features during downsampling and have difficulty recovering those features through upsampling, resulting in a high number of false detections and missed detections. In this work, we present a new ship detection algorithm called Synergistic Star-Attentive Network with Dynamic Feature Refinement (S²D-Net). First, we create a new backbone called Multi-scale PCCA-StarNet to generate robust feature representations. Within the backbone we implement a Progressive Channel-Coordinate Attention (PCCA) mechanism to create a synergy between global channel filtering and adaptive coordinate locking to decouple ship textures from granular speckle noise. Second, we create a Dynamic Feature Refinement Neck. We develop a content-aware dynamic upsampler called DySample to replace conventional interpolation to improve fidelity of the upsampled feature of small targets. Further, we design a Star-PCCA Feature Aggregation module which fuses features together. Using star-operations and the PCCA mechanism, this module refines semantic features and removes background clutter while aggregating features across multiple scales. Third, we develop a Lightweight Shared Convolutional Detection Head with Quality Estimation (LSCD-LQE). The LSCD-LQE decreases parameter redundancy by using shared convolutional layers and adds a localization quality estimation branch. Therefore, the LSCD-LQE effectively reduces false positive detections through alignment of classification scores with localization quality based on Intersection over Union (IoU) in difficult coastal environments. Our experimental results, using the SSDD and HRSID datasets, show that S²D-Net produces results comparable to representative ship detection algorithms. In particular, on the challenging HRSID inshore subset, our proposed method achieved a mean average precision (mAP) of 82.7%, which is 6.9% greater than the YOLOv11n baseline ship detection algorithm. These results demonstrate that S²D-Net is superior at detecting small coastal vessels and mitigating the detrimental effects of the nearshore complex environment on the performance ship detection using SAR. Full article

Ship monitoring using Synthetic Aperture Radar (SAR) data faces significant challenges in detecting small vessels due to low spatial resolution and speckle noise. While ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) has shown promise for image super-resolution, it struggles with SAR imagery characteristics. This study proposes SA/SU-ESRGAN, which extends the SU-ESRGAN framework by incorporating a spatial attention mechanism loss function. SU-ESRGAN introduced semantic structural loss to accurately preserve ship shapes and contours; our enhancement adds spatial attention to focus reconstruction efforts on ship regions while suppressing background noise. Experimental results demonstrate that SA/SU-ESRGAN successfully detects small vessels that remain undetectable by SU-ESRGAN, achieving improved detection capabilities with a PSNR of approximately 26 dB (SSIM is around 0.5) and enhanced visual clarity in ship boundaries. The spatial attention mechanism effectively reduces noise influence, producing clearer super-resolution results suitable for maritime surveillance applications. Based on the HRSID dataset, a representative dataset for evaluating ship detection performance using SAR data, we evaluated ship detection performance using images in which the spatial resolution of the SAR data was artificially degraded using a smoothing filter. We found that with a 4 × 4 filter, all eight ships were detected without any problems, but with an 8 × 8 filter, only three of the eight ships were detected. When super-resolution was applied to this, six ships were detected. Full article

Laser Speckle Imaging (LSI) is a non-invasive optical technique used to assess biological activity by detecting dynamic variations in speckle patterns. These patterns exhibit statistical symmetry in static regions, while biological activity induces symmetry breaking that can be captured through the Graphic Absolute Value of Differences (GAVD), producing the activity map $I_{GAVD}$. This work evaluates the effect of four contrast enhancement algorithms: Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Multiscale Morphological Contrast Enhancement (MMCE), and Multiscale Top-Hat Transform with an Open-Close Close-Open (OCCO) filter, applied to intermediate LSI images, with the final activity map used for quantitative evaluation. Each method represents a distinct enhancement paradigm: HE and CLAHE are histogram-based techniques for global and local contrast adjustment, whereas MMCE and OCCO-MTH are morphological approaches that emphasize structural preservation and local detail enhancement. The dataset consisted of images of Phaseolus vulgaris (SP) and Lactuca sativa (SL) seeds. Evaluation was conducted through expert visual inspection and quantitative analysis using contrast, entropy, spatial frequency (SF), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and contrast improvement ratio (CIR). All metrics were computed on $I_{GAVD}$ activity maps, which reflect bioactivity through the disruption of statistical symmetry. Non-parametric statistical tests (Friedman, aligned Friedman, and Quade) revealed that CLAHE and MMCE significantly improved image quality compared to the original images ($p<0.05$). Among the evaluated algorithms, CLAHE increased global contrast by approximately 25% and entropy by 6% relative to the original speckle frames, enhancing the visibility of bioactive regions. MMCE achieved the highest bioactivity contrast ratio (CIR = 0.64), while OCCO-MTH provided the best structural fidelity (SSIM = 0.91) and noise suppression (PSNR = 30.7 dB). These results demonstrate that suitable contrast enhancement can substantially improve the interpretability of LSI activity maps without altering acquisition hardware. This finding is particularly relevant for experimental applications aiming to maximize information quality without modifying acquisition hardware. Full article

Underwater debris poses a significant threat to marine ecosystems, fisheries, and the tourism industry, necessitating the development of automated vision-based detection systems. Although recent studies have sought to enhance detection performance through underwater image enhancement, improvements in visual quality do not necessarily translate into higher detection accuracy and may, in some cases, degrade performance. To address this discrepancy between perceptual quality and detection reliability, we propose DiCAF, a dual-input co-attention fusion network built upon the latest You Only Look Once v11 detector. The proposed architecture processes both original and enhanced images in parallel and fuses their complementary features through a co-attention module, thereby improving detection stability and consistency. To mitigate high-frequency noise amplified during the enhancement process, a lightweight Gaussian filter is applied as a post-processing step, enhancing robustness against speckle noise commonly introduced by suspended particles in underwater environments. Furthermore, DiCAF incorporates a non-maximum suppression (NMS)-based ensemble that integrates detection outputs from three branches—original, enhanced, and fused—enabling complementary detection of objects missed by individual models and maximizing overall detection performance. Experimental results demonstrate that the proposed single-model DiCAF with Gaussian post-processing achieves an AP@0.5 of 0.87 and an AP@0.5:0.95 of 0.71 on a marine trash dataset. With the NMS-based ensemble, performance improves to 0.91 and 0.75, respectively. Under artificially injected speckle noise conditions, the proposed method maintains superior robustness, achieving an AP@0.5 of 0.62 and consistently outperforming conventional enhancement-based models. Full article

This paper presents a statistically grounded algorithm for surface imaging with linear frequency-modulated continuous wave synthetic aperture radar. The approach is based on the maximum likelihood principle, where solving the optimization problem naturally leads to the introduction of a spectral decorrelation filter. The proposed method increases the effective number of statistically independent samples, reduces speckle, and improves the accuracy of radar cross section estimation. Simulation experiments demonstrate consistent advantages over classical SAR processing: the proposed method achieves up to a 21% improvement in feature similarity metrics and an average 4% improvement across standard quantitative image quality measures. Full article

Although GPUs are widely used in Computer-Generated Holography (CGH), their specific application to concrete problems such as occlusion or speckle filtering through temporal multiplexing is not yet standardized and has not been fully explored. This work aims to optimize the software architecture by taking the GPU architecture into account in a novel way for these particular tasks. We present an optimized algorithm for CGH computation that provides a joint solution to the problems of speckle noise and occlusion. The workflow includes the generation and illumination of a 3D scene, the calculation of the CGH including color, occlusion, and temporal speckle-noise filtering, followed by scene reconstruction through both simulation and experimental methods. The research focuses on implementing a temporal multiplexing technique that simultaneously performs speckle denoising and occlusion culling for point clouds, evaluating two types of occlusion that differ in whether the occlusion effect dominates over the depth effect in a scene stored in a CGH, while leveraging the parallel processing capabilities of GPUs to achieve a more immersive and high-quality visual experience. To this end, the total computational cost associated with generating color and occlusion CGHs is evaluated, quantifying the relative contribution of each factor. The results indicate that, under strict occlusion conditions, temporal multiplexing filtering does not significantly impact the overall computational cost of CGH calculation. Full article

Aiming at the challenges faced by the detection of small-scale ship targets in Synthetic Aperture Radar (SAR) images, this paper proposes a novel deep learning network named FAWT-Net based on attention-matrix despeckling and Haar wavelet reconstruction. This network collaboratively optimizes the detection performance through three core modules. First, during the feature transfer stage from backbone to the neck, a filtering module based on attention matrix is designed, which can suppress the speckle noise. Then, during feature upsampling stage, a wavelet transform feature upsampling method for reconstructing image details is designed to enhance the distinguishability of target boundaries and textures. At the same time, the network also combines sub-image feature stitching downsampling to avoid losing key details in small targets, and adopts a scale-sensitive detection head. By adaptively adjusting the shape constraints of prediction boxes, it effectively solves the regression deviation problem of ship targets with inconsistent aspect ratios. Verified by experiments on SSDD and LS-SSDD, the proposed method improves AP50 by 1.3% and APS by 0.8% on the SSDD. Meanwhile, it is verified that the proposed method has higher precision and recall rates on the LS-SSDD, and the recall rate has been increased by 2.2%. Full article

A common drawback of denoising methods of images is that all pixels are filtered regardless of the amount of noise affecting them individually. Since the essence of denoising is lowpass filtering, subjecting clean pixels to denoising results in blurring. In this paper, a filtering framework is introduced where a fitness function is incorporated in a Kalman filter (KF) to assess the suitability of accepting the value recommended by KF or retaining the existing value of a pixel. Furthermore, a limit on the number of iterations is imposed to avoid over filtering that leads to shrinkage of pixel value ranges of the channels and loss of spectral signatures. In post processing, the means of the filtered channels are shifted to their original values prior to filtering, to spread the pixel value ranges and regain important spectral signatures. The experiments involve the implementation of KF, extended Kalman filter (EKF), Kalman smoother (KS), extended Kalman smoother (EKS) and moving average filter (MAF) in filtering noisy channels of oil palm hyperspectral data under the same framework. Their performances are compared in terms of execution time, SNR gain, NIQE and SSIM metrics. In the second set of experiments, the performance of the improved KF with a fitness function and mean restoration is compared to those of KF and MAF. The results show that the improved KF outperforms the other two filters in the spectral signature characteristics and pixel value ranges of the denoised channels. Full article

Ship detection in synthetic aperture radar (SAR) remote sensing imagery is of great significance in military and civilian applications. However, two factors limit detection performance: (1) a high prevalence of small-scale ship targets with limited information content and (2) interference affecting ship detection from speckle noise and land–sea clutter. To address these challenges, we propose a novel end-to-end (E2E) transformer-based SAR ship detection framework, called Flow-Aligned Nested Transformer for SAR Small Ship Detection (FANT-Det). Specifically, in the feature extraction stage, we introduce a Nested Swin Transformer Block (NSTB). The NSTB employs a two-level local self-attention mechanism to enhance fine-grained target representation, thereby enriching features of small ships. For multi-scale feature fusion, we design a Flow-Aligned Depthwise Efficient Channel Attention Network (FADEN). FADEN achieves precise alignment of features across different resolutions via semantic flow and filters background clutter through lightweight channel attention, further enhancing small-target feature quality. Moreover, we propose an Adaptive Multi-scale Contrastive Denoising (AM-CDN) training paradigm. AM-CDN constructs adaptive perturbation thresholds jointly determined by a target scale factor and a clutter factor, generating contrastive denoising samples that better match the physical characteristics of SAR ships. Finally, extensive experiments on three widely used open SAR ship datasets demonstrate that the proposed method achieves superior detection performance, outperforming current state-of-the-art (SOTA) benchmarks. Full article

Background: Speckled lentiginous nevus (SLN), also known as nevus spilus (NS), is a variant of congenital melanocytic nevus. Although historically considered to have low malignant potential, recent studies have reported melanoma arising within SLN. This study presents a systematic review of multiple melanomas occurring in association with SLN and includes a representative clinical case. Methods: We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search of PubMed, Scopus, Web of Science, and Google Scholar was performed from 1957 to 2025 using the terms “melanoma” and “nevus spilus” or “speckled lentiginous nevus.” Filters were applied for original reports, case series, and case reports. Studies were selected based on predefined criteria, with data independently extracted by two reviewers. A case of a 66-year-old male with three melanomas (two within and one outside SLN) over nine years is also presented. Because the evidence base consisted almost exclusively of case reports and small series, meta-analysis and formal risk-of-bias assessment were not feasible; findings were therefore synthesized qualitatively. Results: We first describe an illustrative case of a 66-year-old male who developed three melanomas (two within and one outside SLN) over a nine-year period, underscoring the challenges of diagnosis and long-term monitoring. In the systematic review, we identified 41 eligible publications describing 51 patients, and in our illustrative case, we identified a total of 52 with melanoma on SLN; 9/52 (17.3%) developed multiple melanomas (24 total), and in our illustrative case, we identified a total of 52. Most were male (seven of nine), with the first melanoma diagnosed at a mean age of 52.4 years. The majority (21/24) occurred within SLNs ≥5 cm and were of the superficial spreading type (16/17 where specified). Of 24 tumors, 19 (79.2%) were synchronous, and among the 16 invasive melanomas, the mean Breslow thickness was 1.17 mm (median 0.95 mm, IQR 0.56–1.40 mm). Conclusions: Large or segmental SLNs may carry a clinically relevant risk for developing multiple melanomas. Regular full-body skin examinations and dermoscopic monitoring are recommended for early detection and management. As the synthesis is based mainly on case reports and small series, these conclusions are necessarily descriptive and exploratory, providing a qualitative mapping of the available evidence rather than definitive risk estimates. Full article

Digital holography enables the reconstruction of both 2D and 3D object information from interference patterns captured by digital cameras. A major challenge in this field is speckle noise, which significantly degrades the quality of the reconstructed images. We propose a novel speckle noise reduction method based on 3D adaptive filtering. Our technique processes a stack of holograms, each with an uncorrelated speckle pattern, using an adapted 3D Frost filter. Unlike conventional filtering techniques, our approach exploits statistical adaptivity to enhance noise suppression while preserving fine image details in the reconstructed holograms. Both numerical simulations and optical experiments confirm that our 3D filtering technique significantly enhances reconstruction quality. Specifically, it reduces the normalized standard deviation by up to 40% and improves the structural similarity index by up to 60% compared to classical 2D, 3D median, BM3D, and BM4D filters. Optical experiments validate the method’s effectiveness in practical digital holography scenarios by local and global image quality estimation metrics. These results highlight adaptive 3D filtering as a promising approach for mitigating speckle noise while maintaining structural integrity in digital holography reconstructions. Full article