Infrared small target detection (ISTD) is a fundamental component of infrared search and tracking systems. The low-rank sparse decomposition method has become the mainstream of ISTD due to its broad applicability across various scenarios. However, certain sparse interferences in complex backgrounds may limit the effectiveness of these methods. To solve this problem, we propose a non-local affinity-based robust interference-resistant model (NARIRM) for ISTD. The model leverages the concept of affinity, which denotes the relationship between pixel regions, assuming that interference has stronger affinity with its neighbors than the target. The affinity values are achieved by reformulating an infrared image as the linear combination of foreground and background and using sparse decomposition results as constraints. A suppressor is then derived to reduce the impact of sparse interference by the affinity values. Experimental evaluation on on public datasets demonstrates the proposed method outperforms several state-of-the-art techniques. The code is available at https://github.com/djk1997-jk/NARIRM.

Infrared small target detection (ISTD) is widely used in civilian and military applications. However, ISTD encounters several challenges, including the tendency for small and dim targets to be obscured by complex backgrounds. To address this issue, we propose the Dynamic Attention Transformer Network (DATransNet), which aims to extract and preserve detailed information vital for small targets. DATransNet employs the Dynamic Attention Transformer (DATrans), simulating central difference convolutions (CDC) to extract gradient features. Furthermore, we propose a global feature extraction module (GFEM) that offers a comprehensive perspective to prevent the network from focusing solely on details while neglecting the global information. We compare the network with state-of-the-art (SOTA) approaches and demonstrate that our method performs effectively. Our source code is available at https://github.com/greekinRoma/DATransNet.

Infrared small target detection (ISTD) plays a critical role in a wide range of civilian and military applications. Existing methods suffer from deficiencies in the localization of dim targets and the perception of contour information under dense clutter environments, severely limiting their detection performance. To tackle these issues, we propose a contour-aware and saliency priors embedding network (CSPENet) for ISTD. We first design a surround-convergent prior extraction module (SCPEM) that effectively captures the intrinsic characteristic of target contour pixel gradients converging toward their center. This module concurrently extracts two collaborative priors: a boosted saliency prior for accurate target localization and multi-scale structural priors for comprehensively enriching contour detail representation. Building upon this, we propose a dual-branch priors embedding architecture (DBPEA) that establishes differentiated feature fusion pathways, embedding these two priors at optimal network positions to achieve performance enhancement. Finally, we develop an attention-guided feature enhancement module (AGFEM) to refine feature representations and improve saliency estimation accuracy. Experimental results on public datasets NUDT-SIRST, IRSTD-1k, and NUAA-SIRST demonstrate that our CSPENet outperforms other state-of-the-art methods in detection performance. The code is available at https://github.com/IDIP-Lab/CSPENet.

Medical image segmentation plays a crucial role in many clinical applications. To alleviate the dependency on massive annotations, semi-supervised learning has attracted increasing attention. However, these methods face significant intra-class and inter-class variation and do not fully utilize the critical multi-view information inherent in medical images. This study proposes a novel network, CV-Net, which integrates multi-view information for semi-supervised medical image segmentation. Concretely, the network is based on Mean-Teacher architecture which largely narrows the empirical distribution gap between labeled and unlabeled data. The proposed cross-view consistency regularization module incorporates a dual-branch attention architecture to integrate consistent semantics while focusing on details, enhancing feature extraction capabilities. The proposed bi-semantic contrastive learning module leverages limited labels and explore pseudo-labels to define semantically similar regions, enhancing the representation capacity. Experiments conducted on two datasets demonstrated the effectiveness of the proposed network. CV-Net showed significant improvements across four metrics, evident with both 5% and 10% labeled data. Specifically, with 5% labeled data, the mean Dice increased by 1.37%. Compared with previous state-of-the-art methods, CV-Net achieved the best results, notably reducing both intra-class and inter-class errors.

Statistical inference based on lossy or incomplete samples is often needed in research areas such as signal/image processing, medical image storage, remote sensing, signal transmission. In this paper, we propose a nonparametric testing procedure based on samples quantized to $B$ bits through a computationally efficient algorithm. Under mild technical conditions, we establish the asymptotic properties of the proposed test statistic and investigate how the testing power changes as $B$ increases. In particular, we show that if $B$ exceeds a certain threshold, the proposed nonparametric testing procedure achieves the classical minimax rate of testing (Shang and Cheng, 2015) for spline models. We further extend our theoretical investigations to a nonparametric linearity test and an adaptive nonparametric test, expanding the applicability of the proposed methods. Extensive simulation studies {together with a real-data analysis} are used to demonstrate the validity and effectiveness of the proposed tests.