When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach
This addresses the problem of separate processing in computer vision by integrating denoising and high-level tasks, offering a novel joint solution for researchers and practitioners.
The paper tackles the joint handling of image denoising and high-level vision tasks by proposing a deep learning approach that cascades modules for both, achieving state-of-the-art denoising performance and improving high-level task results with more visually appealing outputs.
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.