  Several recent works discussed application-driven image restoration neural
networks, which are capable of not only removing noise in images but also
preserving their semantic-aware details, making them suitable for various
high-level computer vision tasks as the pre-processing step. However, such
approaches require extra annotations for their high-level vision tasks, in
order to train the joint pipeline using hybrid losses. The availability of
those annotations is yet often limited to a few image sets, potentially
restricting the general applicability of these methods to denoising more unseen
and unannotated images. Motivated by that, we propose a segmentation-aware
image denoising model dubbed U-SAID, based on a novel unsupervised approach
with a pixel-wise uncertainty loss. U-SAID does not need any ground-truth
segmentation map, and thus can be applied to any image dataset. It generates
denoised images with comparable or even better quality, and the denoised
results show stronger robustness for subsequent semantic segmentation tasks,
when compared to either its supervised counterpart or classical
"application-agnostic" denoisers. Moreover, we demonstrate the superior
generalizability of U-SAID in three-folds, by plugging its "universal" denoiser
without fine-tuning: (1) denoising unseen types of images; (2) denoising as
pre-processing for segmenting unseen noisy images; and (3) denoising for unseen
high-level tasks. Extensive experiments demonstrate the effectiveness,
robustness and generalizability of the proposed U-SAID over various popular
image sets.
