|Description (include details on usage, files and paper references)||The Darmstadt Noise dataset provides a benchmark for denoising performance. Lacking realistic ground truth data, image denoising
techniques are traditionally evaluated on images corrupted by
synthesized i. i. d. Gaussian noise. This is quite problematic,
since noise in real photographs is not i. i. d. Gaussian and
even seemingly minor details of the synthetic noise process,
such as whether the noisy values are rounded to integers,
can have a significant effect on the relative performance of
Hence, we present a novel denoising benchmark, the Darmstadt Noise Dataset (DND). It consists of 50 pairs of real noisy images
and corresponding ground truth images that were captured with consumer grade cameras of differing sensor sizes. For each pair, a reference image is taken with the base ISO level while the noisy image is taken with higher ISO and appropriately adjusted exposure time. The reference image undergoes a careful post-processing entailing small camera shift adjustment, linear intensity scaling and removal of low frequency bias. The post-processed image serves as ground truth for our denoising benchmark.
+ Benchmark consisting of 50 high-resolution images with
realistic image noise.
+ We used four different consumer cameras with differing
sensor sizes: A Sony A7R (full-frame), an Olympus OMD
E-M10 (Micro Four-Thirds), a Sony RX100 IV (1 inch)
and a Nexus 6P (1/2.3 inch)
+ Scenes include typical photographs as well as
+Data is provided as RAW and sRGB intensities (after
applying custom camera processing pipeline).
+Evaluation is done in RAW space and sRGB space.
Tobias Plötz and Stefan Roth, Benchmarking
Denoising Algorithms with Real Photographs, CVPR 2017