This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ∼250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data. A. Abdelhamed (kamel@eecs.yorku.ca, York University), M. Afifi, R. Timofte, and M.S. Brown are the NTIRE 2020 challenge organizers, while the other authors participated in the challenge. Appendix A contains the authors' teams and affiliations. NTIRE webpage: arXiv:2005.04117v1 [cs.CV] 8 May 2020

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Volatile compounds of shiitake mushroom (Lentinus edodes Sing.) samples were extracted by solid phase microextraction (SPME) and then analyzed by GC and GC-MS. Fresh shiitake contained very low level volatile compounds, the major volatile compounds being: 1-0cten-3-01, 3-0ctanone, dimethyl disulfide and dimethyl trisulfide. During aging, all volatile cornpounds decreased rapidly; Fresh and crushed shiitake contained 6~,000 times Inore C8 compounds than the uncrushed, the 3-0ctanone, 1,2,4-trithiolane, and 1-(methylthio)dimethyl disulfide contents increased and/or then decreased during aging. Drying of shiitake mushrooms caused loss of C8 compounds and straight chain sulfur compounds such as dimethyl disulfide, however, it increased the cyclic sulfur compounds 1,2,4-trithiolane and 1,2,4,5-tetrathiane. Contents of 1,2,4-trithiolane and lenthionine in 40'C or 70'C water-soaked dried shiitake increased significantly during soaking. Changes of volatile compounds in fresh or 70'C water-soaked dried shiitake during heating in boiling water were also reported. Contents of lenthionine decreased significantly after heating in boiling water.

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance. Our code is available at https://github. com/Jiaming-Liu/BayerUnifyAug.