SNIC: Synthesized Noisy Images using Calibration

Training advanced denoising models requires large datasets of high-fidelity, physically accurate images. While heteroscedastic noise models can simulate realistic noise, methodologies for their calibration remain under-explored, and large-scale calibrated datasets are scarce. We present a rigorous calibration and tuning pipeline for building high-quality heteroscedastic noise models across a range of sensors, incorporating dark frames to capture signal-independent noise. When evaluated with a state-of-the-art denoiser, our synthesized noisy RAW images reduce the Peak Signal to Noise Ratio (PSNR) gap to real-world noise by 54-64% compared to synthesized RAW images created using manufacturer-provided noise profiles, which fail to account for smart-phone ISP processing that suppresses noise in RAW files during calibration. Leveraging our pipeline, we introduce the Synthesized Noisy Images using Calibration (SNIC) dataset: over 6600 images across 30 scenes and four sensors (DSLR, point-and-shoot, and smartphone), with open-source calibration code and noise models. To our knowledge, SNIC is the only publicly available dataset with calibrated synthesized noise providing paired RAW and TIFF data, offering a new resource for researchers developing noise reduction models.