A Single-Frequency Autofocusing Method Based on Conditional Diffusion Model for Millimeter-Wave Near-Range Imaging

Active wideband millimeter-wave (MMW) systems enable safe and accurate 3-D microwave imaging for various applications, particularly in security screening, while intricate design and implementation are often employed to acquire wideband measurements. Potential imaging methods utilizing single-frequency measurements can be designed to alleviate the hardware requirements. However, applying conventional near-range imaging algorithms directly to single-frequency measurements leads to defocusing issues and degraded imaging results due to the lack of range resolution. Although previous studies have employed autofocusing methods to achieve focused results from defocused images, further enhancement is required for the imaging quality of extended targets. An innovative method based on the conditional diffusion model, named single-frequency autofocusing diffusion model (SFADiff), is proposed in this paper to address the defocusing problem in single-frequency imaging and reconstruct high-quality images effectively. The single-frequency autofocusing encoder network (SFAENet) is first developed to convert 3-D defocused images into 2-D encoded images. Subsequently, SFADiff utilizes these encoded images as conditions guiding image generation towards producing high-quality focused images. Comprehensive experiments are conducted to demonstrate that SFADiff exhibits exceptional performance in terms of image quality, offering a viable solution for achieving single-frequency imaging through autofocusing.