论文标题
一种生成的对抗方法，剩余的学习灰尘和刮擦伪影清除
A Generative Adversarial Approach with Residual Learning for Dust and Scratches Artifacts Removal
论文作者
论文摘要
修饰可以显着提升照片的视觉吸引力，但许多休闲摄影师缺乏专业的专业知识。旧照片修饰的一项特别具有挑战性的任务是去除灰尘和刮擦伪像。传统上，此任务已使用特殊的图像增强软件手动完成，并且代表了一项繁琐的任务，需要特别了解照片编辑应用程序。 但是，与传统方法相比，使用生成对抗网络（GAN）的最新研究已被证明可以在各种自动化图像增强任务中获得良好的结果。这促使我们在电影照片编辑的背景下探索了gan的使用。在本文中，我们提出了一种基于GAN的方法，该方法能够去除灰尘并从胶片扫描中刮擦错误。具体而言，剩余的学习被用来加快训练过程，并提高降解性能。 对我们在社区提供的模型进行的广泛评估表明，它可以很好地概括，不依赖于任何特定类型的图像。最后，我们大大优于最先进的方法和软件应用程序，提供了卓越的结果。
Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to operate in a professional manner. One particularly challenging task for old photo retouching remains the removal of dust and scratches artifacts. Traditionally, this task has been completed manually with special image enhancement software and represents a tedious task that requires special know-how of photo editing applications. However, recent research utilizing Generative Adversarial Networks (GANs) has been proven to obtain good results in various automated image enhancement tasks compared to traditional methods. This motivated us to explore the use of GANs in the context of film photo editing. In this paper, we present a GAN based method that is able to remove dust and scratches errors from film scans. Specifically, residual learning is utilized to speed up the training process, as well as boost the denoising performance. An extensive evaluation of our model on a community provided dataset shows that it generalizes remarkably well, not being dependent on any particular type of image. Finally, we significantly outperform the state-of-the-art methods and software applications, providing superior results.
