A Diffusion Model based Super-Resolution Imaging Method for Through-Wall Sensing

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 paper 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 multi-source data including the 3D radar images and 2D 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.