Most in-the-wild images are stored in Low Dynamic Range (LDR) form, serving as a partial observation of the High Dynamic Range (HDR) visual world. Despite limited dynamic range, these LDR images are often captured with different exposures, implicitly containing information about the underlying HDR image distribution. Inspired by this intuition, in this work we present, to the best of our knowledge, the first method for learning a generative model of HDR images from in-the-wild LDR image collections in a fully unsupervised manner. The key idea is to train a generative adversarial network (GAN) to generate HDR images which, when projected to LDR under various exposures, are indistinguishable from real LDR images. The projection from HDR to LDR is achieved via a camera model that captures the stochasticity in exposure and camera response function. Experiments show that our method GlowGAN can synthesize photorealistic HDR images in many challenging cases such as landscapes, lightning, or windows, where previous supervised generative models produce overexposed images. We further demonstrate the new application of unsupervised inverse tone mapping (ITM) enabled by GlowGAN. Our ITM method does not need HDR images or paired multi-exposure images for training, yet it reconstructs more plausible information for overexposed regions than state-of-the-art supervised learning models trained on such data.
翻译:以低动态范围(LDR)形式存储了大多数在世图像,作为高动态区域(HDR)视觉世界的部分观察。尽管有有限的动态范围,这些LDR图像往往以不同的曝光方式拍摄,隐含着关于《人类发展报告》图像分布的信息。受这种直觉的启发,我们在此工作中以我们的知识最丰富的方式展示了第一个方法,用以学习从在世动态区域(LDR)图像采集的图象的基因化模型。关键的想法是训练一个基因化对抗网络(GAN)来生成《人类发展报告》图像,这些图像在各种曝光下被投放到LDR时,与真实的LDR图像无法区分。《人类发展报告》对LDR的投影是通过一个照相模型实现的,该模型将捕捉到接触和相机反应功能的随机性。实验表明,我们的方法GlowGAN可以将光现实性人类发展报告图像综合到许多富有挑战性的例子,如景观、闪电或窗口,而以前监督的基因化的模型则生成过度的图像。我们进一步展示了未经监督的图像的新的应用,在高清晰度图像的图像中,但又能够对GDRDRDRDRDM系统进行重建。