The high cost of synthetic aperture radar (SAR) data acquisition motivates SAR image generation research. However, the data scarcity and SAR's inherent azimuth sensitivity make generative models suffer from severe azimuth overfitting. Most existing methods require supplementary data to work effectively, limiting their practicality. In this paper, we propose SAR-DisentDM, a novel semantic-disentangled diffusion model for limited-data SAR image generation, without requiring any auxiliary resources. We develop a physics-aware diffusion architecture that explicitly models semantic knowledge of SAR images, including intrinsic characteristics, contextual diversity, and measurement randomness. A key innovation is the attention-guided semantic disentanglement (AGSD) module, designed to decouple category-specific features from azimuth-variable scattering patterns. This is achieved by aid of a dual disentangled loss with time-step-adaptive optimization. Furthermore, we introduce an azimuth angle perturbation augmentation (AAPA) mechanism, to enhance the model's robustness to minor azimuth angle errors. Extensive evaluations validate that SAR-DisentDM enables controllable SAR image synthesis with designated attributes, significantly improving representation and generalization abilities under limited data. Synthetic imagery from our approach boosts automatic target recognition (ATR) accuracy beyond state-of-the-art methods.

Metal implants in patients cause severe streaking artifacts in computed tomography (CT) images, significantly compromising image quality. Deep learning methods have been successfully applied to metal artifact reduction (MAR) in CT, but often result in overly smooth images, failing to reconstruct complex details accurately. In this paper, we propose a multilevel dual-domain progressive enhancement network with embedded attention for MAR, termed MUPO-Net. Our approach constructs a Contrast Weight Mapping (CWM) module that generates a weighted heatmap, allocating weights to different regions based on the influence of metal artifacts, and an ASR-Net (Attention-Embedded Sinogram Restoration Network) that utilizes these weights to better remove artifacts in sinogram domain. Additionally, an Image Detail Enhancement Network (IDE-Net) is proposed to restore fine texture details in CT images through multi-scale feature fusion. Extensive experiments on both synthetic and clinical datasets demonstrate the superior effectiveness of MUPO-Net compared to the state-of-the-art MAR techniques.

Abstract. Woodall [List colourings of graphs, in Surveys in Combinatorics, Cambridge University Press, Cambridge, UK, 2001, pp. 269–301] (and Seymour independently in [ Discrete Math., 310 (2010), pp. 2637–2654]) proposed a conjecture that every graph [Formula: see text] contains every complete bipartite graph on [Formula: see text] vertices as a minor, where [Formula: see text] is the chromatic number of [Formula: see text]. In this paper, we prove that for each positive integer [Formula: see text] with [Formula: see text], each graph [Formula: see text] with independence number two contains a [Formula: see text]-minor, implying that Seymour and Woodall’s conjecture holds for graphs with independence number two, where [Formula: see text] is the graph obtained from [Formula: see text] by making every pair of vertices on the side of the bipartition of size [Formula: see text] adjacent.