2,647 research outputs found
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
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
Recent developments in deep learning have revolutionized the paradigm of
image restoration. However, its applications on real image denoising are still
limited, due to its sensitivity to training data and the complex nature of real
image noise. In this work, we combine the robustness merit of model-based
approaches and the learning power of data-driven approaches for real image
denoising. Specifically, by integrating graph Laplacian regularization as a
trainable module into a deep learning framework, we are less susceptible to
overfitting than pure CNN-based approaches, achieving higher robustness to
small datasets and cross-domain denoising. First, a sparse neighborhood graph
is built from the output of a convolutional neural network (CNN). Then the
image is restored by solving an unconstrained quadratic programming problem,
using a corresponding graph Laplacian regularizer as a prior term. The proposed
restoration pipeline is fully differentiable and hence can be end-to-end
trained. Experimental results demonstrate that our work is less prone to
overfitting given small training data. It is also endowed with strong
cross-domain generalization power, outperforming the state-of-the-art
approaches by a remarkable margin
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