论文标题
将智能手机转换为数码单反相机：在野外学习ISP
Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild
论文作者
论文摘要
我们提出了一个可训练的图像信号处理（ISP）框架，该框架生成由智能手机捕获的原始图像的DSLR质量图像。为了解决训练图对之间的颜色未对准，我们采用了颜色条件的ISP网络，并优化了每个输入原始和参考DSLR图像之间的新型参数颜色映射。在推断期间，我们通过设计具有有效的全局上下文变压器模块的颜色预测网络来预测目标颜色图像。后者有效地利用全球信息来学习一致的颜色和音调映射。我们进一步提出了一个强大的掩盖对齐损失，以识别和丢弃训练期间运动估计不准确的区域。最后，我们在野外（ISPW）数据集中介绍ISP，由弱配对的手机RAW和DSLR SRGB图像组成。我们广泛评估了我们的方法，在两个数据集上设置了新的最先进的方法。
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.
