Infrared small target detection (IRSTD) remains challenging due to the weak spatial features of targets and their susceptibility to background clutter. Recent studies have improved detection performance through the embedding of additional spatial representations. However, these feature prompting methods rely on discretely sampled feature spaces, which weaken high-frequency information and consequently limit their representational efficiency. To overcome this, we propose NeRI, a network that leverages the potential of implicit neural representations (INRs) through a continuous formulation to learn mappings from spatial coordinates to the high-frequency structural representations of targets. Specifically, these mappings are realized through INR Blocks (INRBs) integrated into different encoder layers, providing continuous spatial guidance from multi-scale inputs and enabling more accurate localization and distinction. In addition, to better model the distinction between foreground and background, we construct a hybrid U-shaped block (HUB) that combines a U-shaped Transformer block (UTB) and multi-scale convolution block (MCB). The UTB component effectively increases network depth and facilitates long-range dependency modeling across different scales, while the MCB employs convolutions with varying receptive fields to capture fine-grained local information, thereby enabling the two components to fully exploit their complementary strengths. Finally, we propose a simple yet effective spatial–semantic fusion (SSF) module that reweights and integrates spatial information from diverse layers to enhance the expressive power of the features. The proposed NeRI offers a robust solution for the accurate separation of targets from backgrounds. Experimental validation, conducted on three public datasets (i.e., NUDT-SIRST, NUAA-SIRST, and IRSTD-1K), demonstrates the superior performance of NeRI compared to other methods. Open-source implementations will be available at https://github.com/Shangwei-Deng/NeRI.

Sparsity and weak characteristics of targets pose significant challenges in infrared small target detection (IRSTD). For convolutional neural network-based methods, the increase in semantic information during propagation is often accompanied by the degradation of spatial features, which hampers the performance of IRSTD. In this paper, we proposed the Inner-outer Variation Synergy Network (IOVarNet) for IRSTD, which explicitly enhances the spatial response of targets by reinforcing their structural representations across different layers of the network. Specifically, IOVarNet leverages the delicate Total Variation-inspired Module, which takes the form of a partial differential equation, and incorporates it through the Inner-outer Variational Synergy architecture to supplement the target’s structural information at both the inner and outer layers of the encoder and decoder. Besides, the Dual Space Attention mechanism was introduced to enhance the semantic distinction between the target and background, while fusing spatial features from different layers. Experimental validation was conducted on three public datasets (i.e., NUDT-SIRST, NUAA-SIRST, and IRSTD-1K), demonstrating the performance superiority of IOVarNet over other methods. Open-source implementations will be available at https://github.com/Shangwei-Deng/IOVarNet.

In the field of infrared small target detection, targets generally exhibit dim characteristics, and difficult to distinguish from background clutter. Learning-based methods enhance feature representation through layer-by-layer propagation, but the sparse target information often diminishes. To address this, we propose DWTFreqNet, a network that splits input data to enhance both local saliency and global contextual differences. It incorporates complementary feature extraction modules designed to match the data distribution characteristics. Specifically, it first utilizes the discrete wavelet transform (DWT) to decompose the input data into low- and high-frequency components. For the low-frequency part, which carries key target information, we apply component-differential dense connections and DWT-based downsampling to maintain feature integrity. For the high-frequency part, rich in target-background contrast, an Adaptive Wavelet Guidance Mechanism optimizes multi-component fusion via adaptive weighting, while a Layer-wide Discrepancy Relationship Capture Module enhances target discrimination by linking multi-scale feature maps. Comparative experiments on public datasets demonstrate its superiority over state-of-the-art methods. The code will be available at https://github.com/Kingwin97/DWTFreqNet.

Undersampling -space data in magnetic resonance imaging (MRI) reduces scan time but pose challenges in image reconstruction. Considerable progress has been made in reconstructing accelerated MRI. However, restoration of high-frequency image details in highly undersampled data remains challenging. To address this issue, we propose CAMP-Net, an unrolling-based Consistency-Aware Multi-Prior Network for accelerated MRI reconstruction. CAMP-Net leverages complementary multi-prior knowledge and multi-slice information from various domains to enhance reconstruction quality. Specifically, CAMP-Net comprises three interleaved modules for image enhancement, -space restoration, and calibration consistency, respectively. These modules jointly learn priors from data in image domain, -domain, and calibration region, respectively, in data-driven manner during each unrolled iteration. Notably, the encoded calibration prior knowledge extracted from auto-calibrating signals implicitly guides the learning of consistency-aware -space correlation for reliable interpolation of missing -space data. To maximize the benefits of image domain and -domain prior knowledge, the reconstructions are aggregated in a frequency fusion module, exploiting their complementary properties to optimize the trade-off between artifact removal and fine detail preservation. Additionally, we incorporate a surface data fidelity layer during the learning of -domain and calibration domain priors to prevent degradation of the reconstruction caused by padding-induced data imperfections. We evaluate the generalizability and robustness of our method on three large public datasets with varying acceleration factors and sampling patterns. The experimental results demonstrate that our method outperforms state-of-the-art approaches in terms of both reconstruction quality and mapping estimation, particularly in scenarios with high acceleration factors.

Underwater distance/length sensing serves as a fundamental function and plays a crucial role in a wide range of applications, including topography and geomorphology in remote sensing. Advanced light detection and ranging (LiDAR) systems and algorithms are significant for underwater tasks, such as objection positing, target searching, and rescuing. In this paper, we propose a dual-comb interferometer-based LiDAR system to achieve accurate underwater absolute distance measurement. Specifically, the system applies a well-designed pulse coding strategy that significantly expands the non-ambiguity range for underwater distance measurements, allowing for precise long-distance measurements in a single attempt. Besides, we introduce a correction process to handle the water group refractive index problem. Compared with reference values, experimental results show that the range fluctuation is within ±15 μm at a 12 m measurement range, and the Allan deviation is 0.62 μm over an averaging time of 100 s. In practice, this LiDAR solution with a micrometer-level precision is promising for various applications in ocean engineering.