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When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach
Conventionally, image denoising and high-level vision tasks are handled
separately in computer vision. In this paper, we cope with the two jointly and
explore the mutual influence between them. First we propose a convolutional
neural network for image denoising which achieves the state-of-the-art
performance. 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
demonstrate 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 can generate more visually appealing results. To the best of
our knowledge, this is the first work investigating 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.Comment: the 27th International Joint Conference on Artificial Intelligence
(2018
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