Wild-JDD is a novel learning framework for joint demosaicking and denoising in the wild . It takes into account ground truth uncertainty, taking into account the quality of training data . The network outperforms state-of-the-art schemes on tasks on both synthetic and realistic rawdatasets . It enjoys good interpretability during optimization, according to the authors of the paper . The paper concludes that Wild-DD is an effective tool for fine-tuning and fine-tuneing tasks on realistic and synthetic raw data . It is published in Springer Springer Springer, Springer Springer and New York University of New York City, New York, NY, NY-U.S.-U.N.-New York-Ulin, N.U.Ulin and N.Y-Report: Springer Springer are published by New York-Harvard University, Springer, MIT-University of the New York State University, respectively, Springer.

Author(s) : Jierun Chen, Song Wen, S. -H. Gary Chan

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Keywords : springer - york - wild - university - paper -

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