Over the past few decades, video quality assessment (VQA) has become a valuable research field. The perception of in-the-wild video quality without reference is mainly challenged by hybrid distortions with dynamic variations and the movement of the content. In order to address this barrier, we propose a no-reference video quality assessment (NR-VQA) method that adds the enhanced awareness of dynamic information to the perception of static objects. Specifically, we use convolutional networks with different dimensions to extract low-level static-dynamic fusion features for video clips and subsequently implement alignment, followed by a temporal memory module consisting of recurrent neural networks branches and fully connected (FC) branches to construct feature associations in a time series. Meanwhile, in order to simulate human visual habits, we built a parametric adaptive network structure to obtain the final score. We further validated the proposed method on four datasets (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC) to test the generalization ability. Extensive experiments have demonstrated that the proposed method not only outperforms other NR-VQA methods in terms of overall performance of mixed datasets but also achieves competitive performance in individual datasets compared to the existing state-of-the-art methods.

Oriented object detection has become a hot topic in SAR image interpretation. Due to the unique imaging mechanism, SAR objects are represented as clusters of scattering points surrounded by coherent speckle noise, leading to blurred outlines and increased false alarms in complex scenes. To address these challenges, we propose a novel noise-to-convex detection paradigm with a hierarchical framework based on the scattering-keypoint-guided diffusion detection transformer (SKG-DDT), which consists of three levels. At the bottom level, the strong-scattering-region generation (SSRG) module constructs the spatial distribution of strong scattering regions via a diffusion model, enabling the direct identification of approximate object regions. At the middle level, the scattering-keypoint feature fusion (SKFF) module dynamically locates scattering keypoints across multiple scales, capturing their spatial and structural relationships with the attention mechanism. Finally, the convex contour prediction (CCP) module at the top level refines the object outline by predicting fine-grained convex contours. Furthermore, we unify the three-level framework into an end-to-end pipeline via a detection transformer. The proposed method was comprehensively evaluated on three public SAR datasets, including HRSID, RSDD-SAR, and SAR-Aircraft-v1.0. The experimental results demonstrate that the proposed method attains an ${\mathrm{AP}}_{50}$ of 86.5%, 92.7%, and 89.2% on these three datasets, respectively, which is an increase of 0.7%, 0.6%, and 1.0% compared to the existing state-of-the-art method. These results indicate that our approach outperforms existing algorithms across multiple object categories and diverse scenes.

Recently, deep learning network is introduced to terrain segmentation application in polarimetric synthetic aperture radar (PolSAR) images and has achieved remarkable performance. However, interclass and intraclass varieties caused by nonlinear characteristic and intrinsic randomness introduced by speckle are still a challenge for segmentation methods. In this letter, a segmentation framework based on nonlinear statistical learning is introduced to bridge the gap between terrain segmentation in PolSAR and optical images. First, a learnable nonlinear statistical description module (LSDM) is proposed to represent the nonlinear characteristic introduced by coherent speckle, which supplements the lack of discriminative linear feature in terrain blocks. Second, a conditional diffusion pipeline is introduced to model latent distributions and perceive intrinsic randomness that causes intraclass variety. Finally, the proposed framework adopts the statistical features learned from nonlinear statistical description module as a condition to align with the process of distribution modeling in the conditional diffusion segmentation pipeline (CDSP). Extensive experiments are conducted on two authoritative PolSAR terrain segmentation datasets, which present competitive performance.

Recently, detectors based on deep learning have boosted the state-of-the-art of application on ship detection in synthetic aperture radar (SAR) images. However, constructing discriminative feature from scattering of background and distinguishing contour of ship precisely still present challenging subject to the inherent scattering mechanism of SAR. In this article, a dual-branch detection framework with perception of scattering characteristic and geometric contour is introduced to deal with the problem. First, a scattering characteristic perception branch is proposed to fit the scattering distribution of SAR ship through conditional diffusion model, which introduces learnable scattering feature. Second, a convex contour perception branch is designed as two-stage coarse-to-fine pipeline to delimit the irregular boundary of ship by learning scattering key points. Finally, a cross-token integration module following Bayesian framework is introduced to couple features of scattering and texture adaptively to learn construction of discriminative feature. Furthermore, comprehensive experiments on three authoritative SAR datasets for oriented ship detection demonstrate the effectiveness of proposed method.

Background: White matter hyperintensity (WMH) is a hallmark of cerebral small vessel disease and an important contributor to vascular cognitive impairment (VCI), yet lesion burden incompletely explains interindividual variability in cognitive outcomes across the VCI continuum. Functional connectome signatures relevant to this variability remain incompletely characterized. Methods: We analyzed multicenter resting-state functional MRI data from 247 participants spanning vascular risk factors with normal cognition, vascular mild cognitive impairment, and vascular dementia. Exploratory external testing was performed in an independent dataset of 37 participants. Connectome-based predictive modeling (CPM) with permutation testing was used. To reduce circularity, we computed cross-validated network strength (cvNS) using predictive masks defined within training folds only. Results: We identified cross-validated functional connectivity patterns associated with MoCA (positive model, p_perm = 0.034) and TMT-B performance (negative model, p_perm = 0.001). These patterns were most prominently represented in Frontoparietal, Motor, and Subcortical–Cerebellar regions. The TMT-B negative network showed a substantial contribution from weak between-network connections (64.4% of predictive edges). In the external dataset, network strength computed from discovery consensus masks remained associated with MoCA. Greater WMH volume was associated with worse TMT-B performance, and mediation analyses indicated that cvNS computed from the TMT-B connectivity pattern statistically linked WMH volume to both TMT-B (indirect = 0.090, 95% CI [0.018, 0.225], p = 0.023) and MoCA performance (indirect = −0.007, 95% CI [−0.019, −0.001], p = 0.031). Conclusions: CPM-derived functional connectivity patterns capture meaningful continuous variability in cognition across the VCI continuum and provide statistical support consistent with WMH-related functional disconnection as a network-level correlate of cognitive impairment.

Ship detection achieves great significance in remote sensing of synthetic aperture radar (SAR) and many efforts have been done in recent years. However, distinguishing ship targets precisely from the interference of multiplicative non-Gaussian coherent speckle is still a challenging task due to the discreteness, variability, and nonlinearity of ship scattering features. A detection framework based on hierarchical sampling representation is introduced to alleviate the phenomenon in this article. First, ships in SAR images exhibit multiplicative non-Gaussian coherent speckle, which introduces nonlinear characteristics under the imaging mechanism of SAR. Therefore, a statistical feature learning module is proposed with learnable design to describe the nonlinear representations and expand the feature space. Second, our method designs a convex-hull representation to fit the irregular contours of ships represented by strong scattering points. Third, in order to supervise and optimize the regression of convex-hull representation, a sparse low-rank reassignment module is employed to evaluate the positive samples with SAR mechanism and reassign ones of high quality, which produces better results. Furthermore, experimental results on three authoritative SAR-oriented datasets for ship detection application present the comprehensive performance of our method.