[R] SNIC: Synthesized Noise Dataset In RAW + TIFF Formats (6000+ Images, 4 Sensors, 30 Scenes)

[Disclosure: This is my paper and dataset]

I’m sharing my paper and dataset from my Columbia CS master’s project. SNIC (Synthesized Noisy Images using Calibration) provides images with calibrated, synthesized noise in both RAW and TIFF formats. The code and dataset are publicly available.

**Paper:** https://arxiv.org/abs/2512.15905

**Code:** https://github.com/nikbhatt-cu/SNIC

**Dataset:** https://doi.org/10.7910/DVN/SGHDCP

## The Problem

Advanced denoising algorithms need large, high-quality training datasets. Physics-based statistical noise models can generate these at scale, but there’s limited published guidance on proper calibration methods and few published datasets using well-calibrated models.

## What’s Included

This public dataset contains 6000+ images across 30 scenes with noise from 4 camera sensors:

– iPhone 11 Pro (main and telephoto lenses)

– Sony RX100 IV

– Sony A7R III

Each scene includes:

– Full ISO ranges for each sensor

– Both RAW (.DNG) and processed (.TIFF) versions

## Validation

I validated the calibration approach using two metrics:

**Noise realism (LPIPS):** Our calibrated synthetic noise achieves comparable LPIPS to real camera noise across all ISO levels. Manufacturer DNG models show significantly worse performance, especially at high ISO (up to 15× worse LPIPS).

**Denoising performance (PSNR):** I applied NAFNet to denoise real noisy images, SNIC synthesized images, and images synthesized using DNG noise models. Images denoised from our calibrated synthetic noise achieved superior PSNR compared to those from DNG-based synthetic noise.

## Why It Matters

SNIC provides both the methodology and dataset for building properly calibrated noise models. The dual RAW/TIFF format enables work at multiple stages of the imaging pipeline. All code and data is publicly available.

Happy to answer questions about the methodology, dataset, or results!