Infrared small target detection under complex backgrounds, especially in dense cloud and changeable clutter scenes, has always been a challenging research task. In order to improve the detection ability of small targets under complex backgrounds, an infrared small target detection method based on gradient correlation filtering and gradient contrast measurement (GCF-CM) is proposed in this paper. The infrared gradient vector field (IGVF) of the original image is first constructed through the facet model. Then, considering the unique gradient characteristics of small targets, a gradient correlation filtering (GCF) method is proposed to filter small targets and background clutters. Meanwhile, a gradient contrast measurement (GCM) method is designed to further enhance the intensity of the small target. Finally, after fusing the two response maps, an adaptive threshold is adopted to extract small targets. Experimental results demonstrate that the proposed method can improve the intensity of the small target and suppress clutter sufficiently. In comparison with other excellent methods, the proposed method exhibits a robust detection performance.

The trajectory planning algorithm of underwater vehicle near-bottom cruise is important to scientific investigation, industrial inspection, and military affairs. An autonomous underwater vehicle (AUV) often faces the problems of complex underwater environment and large cruise area in a real environment, and some robots must hide themselves during the cruise. However, to the best of our knowledge, few studies have focused on trajectory planning algorithms for AUVs with multiple constraints on large-scale maps. The currently used algorithms are not effective at solving length-constraint problems, and the mainstream trajectory planning algorithms for robots cannot be directly applied to the needs of underwater vehicle sailing near the bottom. Therefore, we present a bounded ridge-based trajectory planning algorithm (RA*) for an AUV to go on a near-bottom cruise. In the algorithm, we design a safety map based on a spherical structure to ensure the safe operation of the robot. In addressing the length-constraint problem and large-scale map planning problem, this paper proposes a two-stage framework for RA*, which designs map compression and parallel computation using a coarse-fine planning framework to solve the large-scale trajectory planning problem and uses a bounded search method to meet the trajectory planning requirements of length constraint. In this study, experiments based on the virtual ocean ridge are conducted, and the results validate the effectiveness and efficiency of the proposed RA* with MCPC algorithm framework.

Underwater Image Enhancement (UIE) aims to restore image quality by mitigating inherent degradations in underwater imaging systems. While existing learning-based methods show promise, they face limitations in separating and processing frequency components, effectively fusing domain information, and balancing the enhancement of structures and details. To resolve these limitations, we propose CDF-UIE, a novel network that leverages and fuses cross-domain information for mitigating the degradation in underwater images. CDF-UIE first performs domain decoupling of input features using the proposed Spatial-Frequency Decoupling (SFD) block. Then we design an innovative Cross-Domain Fusion (CDF) block, which effectively bridges the spatial and frequency domain features through the cross-domain attention mechanism. To produce stable and detailed enhanced outputs, we exploit the coarse and fine-scale information in the image reconstruction stage. Additionally, we introduce a multiscale objective function that incorporates pixel-level, structural, and perceptual constraints to guide the enhancement process. We conduct extensive experiments on six diverse real-world underwater image datasets. Comprehensive experiments and real-world application tests demonstrate that CDF-UIE significantly outperforms existing methods, offering promising future applications in various underwater scenarios. The source code is available at https://github.com/hpzhan66/CDF-UIE.

The combination of total lymphoid irradiation and anti-T-cell antibodies safely induces immune tolerance to combined hematopoietic cell and organ allografts in humans. Our mouse model required host natural killer T (NKT) cells to induce tolerance. Because NKT cells normally depend on signals from CD8⁺ dendritic cells (DCs) for their activation, we used the mouse model to test the hypothesis that, after lymphoid irradiation, host CD8⁺ DCs play a requisite role in tolerance induction through interactions with NKT cells. Selective deficiency of either CD8⁺ DCs or NKT cells abrogated chimerism and organ graft acceptance. After radiation, the CD8⁺ DCs increased expression of surface molecules required for NKT and apoptotic cell interactions and developed suppressive immune functions, including production of indoleamine 2,3-deoxygenase. Injection of naive mice with apoptotic spleen cells generated by irradiation led to DC changes similar to those induced by lymphoid radiation, suggesting that apoptotic body ingestion by CD8⁺ DCs initiates tolerance induction. Tolerogenic CD8⁺ DCs induced the development of tolerogenic NKT cells with a marked T helper 2 cell bias that, in turn, regulated the differentiation of the DCs and suppressed rejection of the transplants. Thus, reciprocal interactions between CD8⁺ DCs and invariant NKT cells are required for tolerance induction in this system that was translated into a successful clinical protocol. Key Points Tolerance after bone marrow transplantation requires CD8⁺ DCs and NKT-cell interaction. CD8⁺ DCs and NKT cells become tolerogenic after conditioning with total lymphoid irradiation.

Side-scan sonar (SSS) has become an important tool for ocean exploration due to its practicality and reliability. Existing approaches for SSS image classification mainly rely on deep convolutional neural networks (DCNNs). Although effective, DCNN-based methods fail to classify SSS images with specific target samples that are completely absent from the training data. In this work, a global context external-attention network (GCEANet) is proposed to produce pseudo-SSS images corresponding to the absent categories for zero-shot SSS image classification. First, the optical and acoustic images are taken as content and style image inputs to the network, respectively. Second, a global context (GC) block is proposed to extract the context semantics of the style features and fuse them with the content features. Finally, an external attention (EA) block is proposed to extract the potential correlation between content and style features to generate pseudo-SSS images with optical content. In addition, a contrastive loss based on image reconstruction is proposed to optimize the cross-modal feature space fusion. Extensive experiments demonstrate the effectiveness of our approach in efficiently generating realistic pseudo-SSS samples. The experimental results on real SSS sample datasets show that our method outperforms other state-of-the-art (SOTA) methods in the field of zero-shot SSS image classification. The code is available at https://github.com/baizhongyu/GCEANet.

Side-scan sonar (SSS) is attracting increasing attention in ocean exploration for its utility and stability on autonomous underwater vehicles (AUVs). Existing SSS image recognition methods mainly employ deep neural networks (DNNs) for various tasks. However, the effectiveness of DNN-based approaches is limited in situations with zero samples. In this letter, a multi-feature fusion self-attention network (MFSANet) is proposed to generate SSS images of novel categories, transforming this problem into a conventional supervised learning problem. Specifically, optical-acoustic image pairs are used as inputs to the network to synthesize pseudo-SSS images. First, the shallow and deep features of the input images are extracted by different layers of the encoder. Then, the long-range dependencies of the acoustic images are efficiently modeled with the proposed simplified self-attention module (SSAM). Finally, the acoustic features are appropriately aggregated to each position of the optical features to efficiently generate pseudo-SSS samples for training the classification network. In addition, a novel contrastive loss is proposed to optimize the cross-modal feature space distribution. Experimental results demonstrate that our method can efficiently generate high-quality pseudo-SSS samples, which improves the accuracy of zero-shot SSS image recognition.