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Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis
Computer Vision and Pattern Recognition · Submitted 24 Mar 2025
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This work establishes a benchmark for polarization burst super-resolution and offers critical insights into noise propagation, thereby enhancing polarization image reconstruction.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Snapshot polarization imaging calculates polarization states from linearly polarized subimages. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s1 | To achieve this, a polarization camera employs a double Bayer-patterned sensor to capture both color and polarization. | background 0.96 | 0.06 | 0.02 | asserted 1.00 |
| s2 | It demonstrates low light efficiency and low spatial resolution, resulting in increased noise and compromised polarization measurements. | background 0.98 | 0.13 | 0.03 | asserted 1.00 |
| s3 | 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. | gap 0.98 | 0.08 | 0.04 | asserted 0.96 |
| s4 | To address these issues, we introduce PolarNS and PolarBurstSR, two innovative datasets developed specifically for polarization imaging. | contribution 1.00 | 0.95 | 0.06 | asserted 1.00 |
| s5 | PolarNS provides characterization of polarization noise statistics, facilitating thorough analysis, while PolarBurstSR functions as a benchmark for burst super-resolution in polarization images. | contribution 0.97 | 0.91 | 0.11 | asserted 1.00 |
| s6 | These datasets, collected under various real-world conditions, enable comprehensive evaluation. | contribution 0.58 | 0.82 | 0.04 | asserted 1.00 |
| s7 | Additionally, we present a model for analyzing polarization noise to quantify noise propagation, tested on a large dataset captured in a darkroom environment. | method 0.51 | 0.96 | 0.04 | asserted 1.00 |
| s8 | 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. | result 0.65 | 0.92 | 0.09 | asserted 1.00 |
| s9 | This work establishes a benchmark for polarization burst super-resolution and offers critical insights into noise propagation, thereby enhancing polarization image reconstruction. | contribution 0.60 | 0.83 | 0.88 | asserted 1.00 |
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Original abstract
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.
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