PVDD: A Practical Video Denoising Dataset with Real-World Dynamic Scenes

• URL: http://arxiv.org/abs/2207.01356v1
• Date: Mon, 4 Jul 2022 12:30:22 GMT
• Title: PVDD: A Practical Video Denoising Dataset with Real-World Dynamic Scenes
• Authors: Xiaogang Xu, Yitong Yu, Nianjuan Jiang, Jiangbo Lu, Bei Yu, Jiaya Jia
• Abstract summary: "Practical Video Denoising dataset" (PVDD) contains 200 noisy-clean dynamic video pairs in both sRGB and RAW format. Compared with existing datasets consisting of limited motion information,PVDD covers dynamic scenes with varying natural motion.
• Score: 56.4361151691284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To facilitate video denoising research, we construct a compelling dataset, namely, "Practical Video Denoising Dataset" (PVDD), containing 200 noisy-clean dynamic video pairs in both sRGB and RAW format. Compared with existing datasets consisting of limited motion information, PVDD covers dynamic scenes with varying and natural motion. Different from datasets using primary Gaussian or Poisson distributions to synthesize noise in the sRGB domain, PVDD synthesizes realistic noise from the RAW domain with a physically meaningful sensor noise model followed by ISP processing. Moreover, based on this dataset, we propose a shuffle-based practical degradation model to enhance the performance of video denoising networks on real-world sRGB videos. Extensive experiments demonstrate that models trained on PVDD achieve superior denoising performance on many challenging real-world videos than on models trained on other existing datasets.

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