Feng Xu

Yanni Wang, Hecheng Jia, Shilei Fu et al.

The electromagnetic inverse problem has long been a research hotspot. This study aims to reverse radar view angles in synthetic aperture radar (SAR) images given a target model. Nonetheless, the scarcity of SAR data, combined with the intricate background interference and imaging mechanisms, limit the applications of existing learning-based approaches. To address these challenges, we propose an interactive deep reinforcement learning (DRL) framework, where an electromagnetic simulator named differentiable SAR render (DSR) is embedded to facilitate the interaction between the agent and the environment, simulating a human-like process of angle prediction. Specifically, DSR generates SAR images at arbitrary view angles in real-time. And the differences in sequential and semantic aspects between the view angle-corresponding images are leveraged to construct the state space in DRL, which effectively suppress the complex background interference, enhance the sensitivity to temporal variations, and improve the capability to capture fine-grained information. Additionally, in order to maintain the stability and convergence of our method, a series of reward mechanisms, such as memory difference, smoothing and boundary penalty, are utilized to form the final reward function. Extensive experiments performed on both simulated and real datasets demonstrate the effectiveness and robustness of our proposed method. When utilized in the cross-domain area, the proposed method greatly mitigates inconsistency between simulated and real domains, outperforming reference methods significantly.

Tao Song, Yicheng Wu, Minhao Hu et al.

Accelerated MRI reconstruction plays a vital role in reducing scan time while preserving image quality. While most existing methods rely on complex-valued image-space or k-space data, these formats are often inaccessible in clinical practice due to proprietary reconstruction pipelines, leaving only magnitude images stored in DICOM files. To address this gap, we focus on the underexplored task of magnitude-image-based MRI reconstruction. Recent advancements in diffusion models, particularly denoising diffusion probabilistic models (DDPMs), have demonstrated strong capabilities in modeling image priors. However, their task-agnostic denoising nature limits performance in source-to-target image translation tasks, such as MRI reconstruction. In this work, we propose a novel Self-Consistent Nested Diffusion Bridge (SC-NDB) framework that models accelerated MRI reconstruction as a bi-directional image translation process between under-sampled and fully-sampled magnitude MRI images. SC-NDB introduces a nested diffusion architecture with a self-consistency constraint and reverse bridge diffusion pathways to improve intermediate prediction fidelity and better capture the explicit priors of source images. Furthermore, we incorporate a Contour Decomposition Embedding Module (CDEM) to inject structural and textural knowledge by leveraging Laplacian pyramids and directional filter banks. Extensive experiments on the fastMRI and IXI datasets demonstrate that our method achieves state-of-the-art performance compared to both magnitude-based and non-magnitude-based diffusion models, confirming the effectiveness and clinical relevance of SC-NDB.