Modeling image and video distortions is an important, but difficult problem of great consequence to numerous and diverse image processing and computer vision applications. While many statistical models have been proposed to synthesize different types of image noise, real-world distortions are far more difficult to emulate. Toward advancing progress on this interesting problem, we consider distortion generation as an image-to-image transformation problem, and solve it via a data-driven approach. Specifically, we use a conditional generative adversarial network (cGAN) which we train to learn four kinds of realistic distortions. We experimentally demonstrate that the learned model can produce the perceptual characteristics of several types of distortion.

中文翻译：

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学习使用生成对抗网络扭曲图像


图像和视频失真建模是一个重要但困难的问题，对众多不同的图像处理和计算机视觉应用产生重大影响。虽然已经提出了许多统计模型来合成不同类型的图像噪声，但现实世界的失真要模拟起来要困难得多。为了推进这个有趣问题的进展，我们将失真生成视为图像到图像的转换问题，并通过数据驱动的方法来解决它。具体来说，我们使用条件生成对抗网络（cGAN）来训练它来学习四种现实扭曲。我们通过实验证明，学习的模型可以产生几种类型的失真的感知特征。