Active Defocus Blurring for Background Clutter Suppression in LWIR DoFP Polarimeter

Long-wave infrared polarimetric imaging plays a crucial role in target detection, material classification, and scene understanding in complex environments. However, high-intensity background clutter significantly degrades the image quality captured by division-of-focal-plane (DoFP) polarimeters, thereby limiting the performance of subsequent target detection algorithms. Meanwhile, conventional post-processing methods passively suppress background clutter through image analysis alone, lacking in-depth exploration of the underlying hardware framework. As a result, their clutter suppression capability remains limited in complex scenes. In this paper, we propose Active Defocus Blurring Enhancement (ADBE), a simple yet effective approach that integrates both hardware and algorithm strategies to flexibly manipulate the imaging process. Specifically, we reveal how the lens defocus influences the measurement of polarization information and the suppression of background clutter. Building on this insight, an active defocus blurring strategy is developed, transforming a conventional imaging limitation into a powerful mechanism for optimizing imaging parameters based on scene conditions. The outdoor experimental results demonstrate that, compared with conventional in-focus approach, the proposed method can significantly suppress the background clutter and enhance the contrast between the target and background, particularly in degree of linear polarization (DoLP) images. These findings highlight the potential of ADBE for next-generation intelligent imaging systems that autonomously adapt to diverse application scenarios.