Journal of Imaging

Underwater robotics platforms are equipped with onboard cameras for navigation and mapping within deep-sea environments, either assisted by an operator in human-in-the-loop missions or automatically through real-time processing of live video. The impairment of vision by turbidity can be addressed by deploying the recent generation of 2-D multi-beam forward-looking imaging sonars, but this comes with limitations; e.g., sonar video data are not readily interpretable by an untrained human operator and cannot be effectively processed by the state-of-the-art computer methods. We propose a framework to translate underwater sonar imagery into intuitive optical views. By training a conditional generative adversarial network (Nvidia Pix2PixHD) on paired clear-water sonar and optical datasets, our model can synthesize high-quality optical images from sonar data captured in murky conditions. Evaluations across two distinct datasets—testing variables like training size, image resolution, field of view, color relevance, and cross-device transferability—demonstrate that our approach performs robustly in practical real-world scenarios.

Video anomaly detection (VAD) has evolved from handcrafted feature-based methods to deep learning and foundation model-based approaches, enabling improved detection of complex and previously unseen events. This review examines this evolution through four paradigms: closed-set, open-set, open-vocabulary, and open-world VAD. A unified taxonomy is developed to organize representative methods according to learning paradigms, model architectures, supervision strategies, and semantic capabilities. The review further compares benchmark datasets, evaluation protocols, and performance metrics, with emphasis on the challenges of detecting and recognizing unseen anomalies in realistic environments. Recent advances in vision–language models (VLMs) are discussed in terms of their ability to incorporate language knowledge and open-ended semantic reasoning into VAD. Key challenges related to generalization, computational efficiency, multimodal understanding, and real-world deployment are identified. Finally, emerging directions involving large language models (LLMs), multimodal large language models (MLLMs), and video large language models (Video-LLMs), together with agentic AI and autonomous reasoning, are discussed toward more adaptive and interpretable VAD systems.

Existing computed tomography (CT) fracture-risk models typically output one scalar score per vertebra without localising the predicted change. We built and internally validated a three-dimensional (3D) convolutional network forecasting, from one baseline acquisition, a per-voxel map of apparent inter-scan bone-mask loss per lumbar vertebra. Paired CT studies from 65 patients yielded 298 baseline-unfractured vertebra patches (L1 to L5); every patient sustained at least one reader-adjudicated incident fracture, so the cohort is outcome-enriched. The endpoint was apparent vertebral-body mask loss between scans after rigid alignment of automated masks. A multi-task 3D residual U-Net with vertebral-level conditioning was compared with a gradient-boosted classifier on the same features and folds, under five-fold patient-grouped cross-validation with clustered bootstrap intervals. Pooled precision-recall area was 0.445 vs. 0.439 for the reference ($+0.005$, 95% confidence interval $-0.018$ to $+0.028$) and pooled area under the curve (AUC) was 0.815. At matched predicted volume, the network map was less fragmented (2.3 vs. 5.3 components per 1000 voxels), and the endpoint separated incident fractures (AUC 0.865). Voxel-level forecasting of apparent inter-scan vertebral change is feasible on routine CT; the contribution is the paired-scan forecasting framework and the spatial-profile difference, not better discrimination. The endpoint is an algorithmic surrogate requiring voxel-level expert adjudication and external validation before clinical use.