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A Diffusion Model Based Super-Resolution Imaging Method for Through-Wall Sensing
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DOI：10.1109/TAES.2025.3560611.png)
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Abstract
En 中文
Through-wall sensing (TWS) systems have extensive applications in civilian as well as military fields because of its ability to detect the obscured space behind obstacles. To harvest good penetrability at a low cost, existing TWS systems often use sparse array configuration operating in the L/S electromagnetic wave band, which poses a poor spatial resolution in the radar images. As a result, it is difficult for end-users to identify a target from the through-wall radar images because the lack of geometry-related information such as outline, shape, etc. In response to this challenge, this article proposes a high-resolution TWS imaging method by the conditional denoising diffusion probabilistic model (DDPM). First, we design a hybrid encoder to extract and fuse the feature from multisource data including the 3-D radar images and 2-D optical images. The extracted features are fed into the network consisting of residual and self-attention modules to predict/estimate the noise, which is then subtracted from the current image. Finally, by estimating and subtracting the noise iteratively, we can obtain the high-resolution image. Simulations and real-world experiments confirm the efficiency of the proposed method in successfully reconstructing the outline and contour information of the target, which outperforms most existing TWS systems in resolution aspect.
Keywords:
Conditional denoising diffusion probabilistic model (DDPM)
super-resolution
through-wall sensing (TWS)
Journal
IF:
5.7
Papers:
686
Citations:
2.4W