# Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis

arXiv: https://arxiv.org/abs/2503.18705 · HTML: https://mlsanity.com/paper/2503.18705/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 83 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 26 of 26)

- [Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model](https://mlsanity.com/paper/1904.00523/) (2019)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [Handheld Multi-Frame Super-Resolution](https://mlsanity.com/paper/1905.03277/) (2019)
- [Deep Burst Super-Resolution](https://mlsanity.com/paper/2101.10997/) (2021)
- [Polarized Reflection Removal with Perfect Alignment in the Wild](https://mlsanity.com/paper/2003.12789/) (2020)
- [Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset](https://mlsanity.com/paper/1908.00682/) (2019)
- [Deep Polarization Imaging for 3D shape and SVBRDF Acquisition](https://mlsanity.com/paper/2105.02875/) (2021)
- [Shape from Polarization for Complex Scenes in the Wild](https://mlsanity.com/paper/2112.11377/) (2021)
- [PANDORA: Polarization-Aided Neural Decomposition Of Radiance](https://mlsanity.com/paper/2203.13458/) (2022)
- [Deep Reparametrization of Multi-Frame Super-Resolution and Denoising](https://mlsanity.com/paper/2108.08286/) (2021)
- [BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment](https://mlsanity.com/paper/2204.08332/) (2022)
- [Burstormer: Burst Image Restoration and Enhancement Transformer](https://mlsanity.com/paper/2304.01194/) (2023)
- [Depth from a polarisation + RGB stereo pair](https://mlsanity.com/paper/1903.12061/) (2019)
- [Polarimetric Multi-View Inverse Rendering](https://mlsanity.com/paper/2007.08830/) (2020)
- [NeISF: Neural Incident Stokes Field for Geometry and Material Estimation](https://mlsanity.com/paper/2311.13187/) (2023)
- [NeRSP: Neural 3D Reconstruction for Reflective Objects with Sparse Polarized Images](https://mlsanity.com/paper/2406.07111/) (2024)
- [High-Res Facial Appearance Capture from Polarized Smartphone Images](https://mlsanity.com/paper/2212.01160/) (2022)
- [Separating Reflection and Transmission Images in the Wild](https://mlsanity.com/paper/1712.02099/) (2017)
- [All-photon Polarimetric Time-of-Flight Imaging](https://mlsanity.com/paper/2112.09278/) (2021)
- [Towards Real-World Burst Image Super-Resolution: Benchmark and Method](https://mlsanity.com/paper/2309.04803/) (2023)
- [Linear Differential Constraints for Photo-polarimetric Height Estimation](https://mlsanity.com/paper/1708.07718/) (2017)
- [Simultaneous Acquisition of High Quality RGB Image and Polarization Information using a Sparse Polarization Sensor](https://mlsanity.com/paper/2209.13106/) (2022)
- [Spectral and Polarization Vision: Spectro-polarimetric Real-world Dataset](https://mlsanity.com/paper/2311.17396/) (2023)
- [Polarization Wavefront Lidar: Learning Large Scene Reconstruction from Polarized Wavefronts](https://mlsanity.com/paper/2406.03461/) (2024)
- [BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical Flow](https://mlsanity.com/paper/2409.15384/) (2024)
- [Polarimetric BSSRDF Acquisition of Dynamic Faces](https://mlsanity.com/paper/2501.01980/) (2024)

## Built on by (most cited first, 3 of 3)

- [Polarization Uncertainty-Guided Diffusion Model for Color Polarization Image Demosaicking](https://mlsanity.com/paper/2602.23847/) (2026)
- [PolarVSR: A Unified Framework and Benchmark for Continuous Space-Time Polarization Video Reconstruction](https://mlsanity.com/paper/2605.10275/) (2026)
- [DensePol: Dense-Angle Polarization Dataset for Learning-Based Polarimetric Vision](https://mlsanity.com/paper/2609.09359/) (2026)

## Abstract (arXiv)

Snapshot polarization imaging calculates polarization states from linearly polarized subimages. To achieve this, a polarization camera employs a double Bayer-patterned sensor to capture both color and polarization. It demonstrates low light efficiency and low spatial resolution, resulting in increased noise and compromised polarization measurements. Although burst super-resolution effectively reduces noise and enhances spatial resolution, applying it to polarization imaging poses challenges due to the lack of tailored datasets and reliable ground truth noise statistics. To address these issues, we introduce PolarNS and PolarBurstSR, two innovative datasets developed specifically for polarization imaging. PolarNS provides characterization of polarization noise statistics, facilitating thorough analysis, while PolarBurstSR functions as a benchmark for burst super-resolution in polarization images. These datasets, collected under various real-world conditions, enable comprehensive evaluation. Additionally, we present a model for analyzing polarization noise to quantify noise propagation, tested on a large dataset captured in a darkroom environment. As part of our application, we compare the latest burst super-resolution models, highlighting the advantages of training tailored to polarization compared to RGB-based methods. This work establishes a benchmark for polarization burst super-resolution and offers critical insights into noise propagation, thereby enhancing polarization image reconstruction.