  In this study, we propose a simple and effective fine-tuning algorithm called
"restore-from-restored", which can greatly enhance the performance of fully
pre-trained image denoising networks. Many supervised denoising approaches can
produce satisfactory results using large external training datasets. However,
these methods have limitations in using internal information available in a
given test image. By contrast, recent self-supervised approaches can remove
noise in the input image by utilizing information from the specific test input.
However, such methods show relatively lower performance on known noise types
such as Gaussian noise compared to supervised methods. Thus, to combine
external and internal information, we fine-tune the fully pre-trained denoiser
using pseudo training set at test time. By exploiting internal self-similar
patches (i.e., patch-recurrence), the baseline network can be adapted to the
given specific input image. We demonstrate that our method can be easily
employed on top of the state-of-the-art denoising networks and further improve
the performance on numerous denoising benchmark datasets including real noisy
images.
