Polarization Denoising and Demosaicking: Dataset and Baseline Method
This work addresses a gap in image processing for polarimetric applications, providing a reproducible baseline for researchers, but it is incremental as it builds on existing signal processing components.
The authors tackled the lack of a dataset and baseline method for joint polarization denoising and demosaicking in division-of-focal-plane polarimeters by proposing a new dataset with 40 real-world scenes and three noise levels, and a method that outperforms alternatives in image reconstruction.
A division-of-focal-plane (DoFP) polarimeter enables us to acquire images with multiple polarization orientations in one shot and thus it is valuable for many applications using polarimetric information. The image processing pipeline for a DoFP polarimeter entails two crucial tasks: denoising and demosaicking. While polarization demosaicking for a noise-free case has increasingly been studied, the research for the joint task of polarization denoising and demosaicking is scarce due to the lack of a suitable evaluation dataset and a solid baseline method. In this paper, we propose a novel dataset and method for polarization denoising and demosaicking. Our dataset contains 40 real-world scenes and three noise-level conditions, consisting of pairs of noisy mosaic inputs and noise-free full images. Our method takes a denoising-then-demosaicking approach based on well-accepted signal processing components to offer a reproducible method. Experimental results demonstrate that our method exhibits higher image reconstruction performance than other alternative methods, offering a solid baseline.