  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.
