Architectural Unification for Polarimetric Imaging Across Multiple Degradations

TL;DR

This paper introduces a unified neural network architecture for polarimetric imaging that effectively handles multiple degradations like noise, blur, and mosaicing, ensuring physical consistency and achieving state-of-the-art results.

Contribution

The work presents a single, adaptable network framework that processes various degradations in polarimetric imaging without task-specific redesigns, maintaining physical relationships.

Findings

Achieves state-of-the-art results in denoising, deblurring, and demosaicing.

Operates in a single stage, reducing error accumulation.

Maintains physical consistency across all tasks.

Abstract

Polarimetric imaging aims to recover polarimetric parameters, including Total Intensity (TI), Degree of Polarization (DoP), and Angle of Polarization (AoP), from captured polarized measurements. In real-world scenarios, these measurements are frequently affected by diverse degradations such as low-light noise, motion blur, and mosaicing artifacts. Due to the nonlinear dependency of DoP and AoP on the measured intensities, accurately retrieving physically consistent polarimetric parameters from degraded observations remains highly challenging. Existing approaches typically adopt task-specific network architectures tailored to individual degradation types, limiting their adaptability across different restoration scenarios. Moreover, many methods rely on multi-stage processing pipelines that suffer from error accumulation, or operate solely in a single domain (either image or Stokes domain), failing to fully exploit the intrinsic physical relationships between them. In this work, we propose a unified architectural framework for polarimetric imaging that is structurally shared across multiple degradation scenarios. Rather than redesigning network structures for each task, our framework maintains a consistent architectural design while being trained separately for different degradations. The model performs single-stage joint image-Stokes processing, avoiding error accumulation and explicitly preserving physical consistency. Extensive experiments show that this unified architectural design, when trained for specific degradation types, consistently achieves state-of-the-art performance across low-light denoising, motion deblurring, and demosaicing tasks, establishing a versatile and physically grounded solution for degraded polarimetric imaging.