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Benchmarking Denoising Algorithms with Real Photographs
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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
doi:10.1109/cvpr.2017.294
dblp:conf/cvpr/PlotzR17
fatcat:ti35cm72mjcpta6au2qtgqne5e