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- W4312851430 abstract "We propose a trainable Image Signal Processing (ISP) framework that produces DSLR quality images given RAW images captured by a smartphone. To address the color misalignments between training image pairs, we employ a color-conditional ISP network and optimize a novel parametric color mapping between each input RAW and reference DSLR image. During inference, we predict the target color image by designing a color prediction network with efficient Global Context Transformer modules. The latter effectively leverage global information to learn consistent color and tone mappings. We further propose a robust masked aligned loss to identify and discard regions with inaccurate motion estimation during training. Lastly, we introduce the ISP in the Wild (ISPW) dataset, consisting of weakly paired phone RAW and DSLR sRGB images. We extensively evaluate our method, setting a new state-of-the-art on two datasets. The code is available at https://github.com/4rdhendu/TransformPhone2DSLR ." @default.
- W4312851430 created "2023-01-05" @default.
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- W4312851430 date "2022-01-01" @default.
- W4312851430 modified "2023-09-26" @default.
- W4312851430 title "Transform Your Smartphone into a DSLR Camera: Learning the ISP in the Wild" @default.
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- W4312851430 doi "https://doi.org/10.1007/978-3-031-20068-7_36" @default.
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