Abstract
Lacking realistic ground truth data, image denoising
techniques are traditionally evaluated on images corrupted
by synthesized i. i. d. Gaussian noise. We aim to obviate
this unrealistic setting by developing a methodology for
benchmarking denoising techniques on real photographs.
We capture pairs of images with different ISO values and
appropriately adjusted exposure times, where the nearly
noise-free low-ISO image serves as reference. To derive the
ground truth, careful post-processing is needed. We correct
spatial misalignment, cope with inaccuracies in the exposure parameters through a linear intensity transform based
on a novel heteroscedastic Tobit regression model, and remove residual low-frequency bias that stems, e.g., from minor illumination changes. We then capture a novel benchmark dataset, the Darmstadt Noise Dataset (DND), with
consumer cameras of differing sensor sizes. One interesting
finding is that various recent techniques that perform well
on synthetic noise are clearly outperformed by BM3D on
photographs with real noise. Our benchmark delineates realistic evaluation scenarios that deviate strongly from those
commonly used in the scientific literature.