Meibin Qi

Recent advancements in road detection using infrared polarization imaging have shown promising results. However, existing methods focus on refined network structures without effectively exploiting infrared polarization imaging mechanisms for enhanced detection. The scarcity of datasets also limits the performance of these methods. In this Letter, we present a denoising diffusion model aimed at improving the performance of road detection in infrared polarization images.

Abstract The fusion of infrared intensity and polarization images can generate a single image with better visible perception and more vital information. Existing fusion methods based on a convolutional neural network (CNN), with local feature extraction, have the limitation of fully exploiting salient target features of polarization. In this Letter, we propose a transformer-based deep network to improve the performance of infrared polarization image fusion.