  Image denoising and high-level vision tasks are usually handled independently
in the conventional practice of computer vision, and their connection is
fragile. In this paper, we cope with the two jointly and explore the mutual
influence between them with the focus on two questions, namely (1) how image
denoising can help improving high-level vision tasks, and (2) how the semantic
information from high-level vision tasks can be used to guide image denoising.
First for image denoising we propose a convolutional neural network in which
convolutions are conducted in various spatial resolutions via downsampling and
upsampling operations in order to fuse and exploit contextual information on
different scales. Second we propose a deep neural network solution that
cascades two modules for image denoising and various high-level tasks,
respectively, and use the joint loss for updating only the denoising network
via back-propagation. We experimentally show that on one hand, the proposed
denoiser has the generality to overcome the performance degradation of
different high-level vision tasks. On the other hand, with the guidance of
high-level vision information, the denoising network produces more visually
appealing results. Extensive experiments demonstrate the benefit of exploiting
image semantics simultaneously for image denoising and high-level vision tasks
via deep learning. The code is available online:
https://github.com/Ding-Liu/DeepDenoising
