Attention-Based Progressive Discrimination Generative Adversarial Networks for Polarimetric Image Demosaicing
Yuxuan Guo, Xiaobing Dai, Shaoju Wang, Guang Jin, Xuemin Zhang
Abstract
Polarization beyond traditional imaging offers a wide range of advantages, encompassing not only the detection of geometric shapes and surfaces, but also the measurement of physical properties. The divided focal plane (DoFP), an ideal real-time imaging, comprises a 2×2 linear polarization filter overlaid on a focal plane array sensor. Consequently, performing polarization demosaicing (PDM) becomes crucial to restore the missing components in the pixel data. The objective of PDM extends beyond the acquisition of intensity, and its primary goal is to minimize the estimation error of polarization characteristics. However, existing demosaicing networks often neglect the issues of polarization spatial-intensity attention and are trained similarly for generalization, thereby failing to effectively distinguish between genuine details and artifacts, which renders demosaicing vulnerable to distortion. In this work, we propose a novel generative adversarial network framework that integrates attention mechanisms and progressive discrimination for polarimetric images. The GAN-based framework introduces a global attention mechanism that enhances the interaction between spatial and polarization intensity information across different regions, resulting in improved network performance. Additionally, the framework generates artifact maps to normalize the model training process. To evaluate the effectiveness of our method, we assess its performance using full-resolution polarimetric image and compare it against traditional and recent PDM methods. The experimental results demonstrate that our proposed method achieves the highest peak signal-to-noise ratio (PSNR) and exhibits excellent visual quality for the output Stokes images when compared to existing methods.