24,658 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
Enhanced CNN for image denoising
Owing to flexible architectures of deep convolutional neural networks (CNNs),
CNNs are successfully used for image denoising. However, they suffer from the
following drawbacks: (i) deep network architecture is very difficult to train.
(ii) Deeper networks face the challenge of performance saturation. In this
study, the authors propose a novel method called enhanced convolutional neural
denoising network (ECNDNet). Specifically, they use residual learning and batch
normalisation techniques to address the problem of training difficulties and
accelerate the convergence of the network. In addition, dilated convolutions
are used in the proposed network to enlarge the context information and reduce
the computational cost. Extensive experiments demonstrate that the ECNDNet
outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack
Deep neural networks (DNNs) have a wide range of applications in the field of
image denoising, and they are superior to traditional image denoising. However,
DNNs inevitably show vulnerability, which is the weak robustness in the face of
adversarial attacks. In this paper, we find some similitudes between existing
deep image denoising methods, as they are consistently fooled by adversarial
attacks. First, denoising-PGD is proposed which is a denoising model full
adversarial method. The current mainstream non-blind denoising models (DnCNN,
FFDNet, ECNDNet, BRDNet), blind denoising models (DnCNN-B, Noise2Noise,
RDDCNN-B, FAN), and plug-and-play (DPIR, CurvPnP) and unfolding denoising
models (DeamNet) applied to grayscale and color images can be attacked by the
same set of methods. Second, since the transferability of denoising-PGD is
prominent in the image denoising task, we design experiments to explore the
characteristic of the latent under the transferability. We correlate
transferability with similitude and conclude that the deep image denoising
models have high similitude. Third, we investigate the characteristic of the
adversarial space and use adversarial training to complement the vulnerability
of deep image denoising to adversarial attacks on image denoising. Finally, we
constrain this adversarial attack method and propose the L2-denoising-PGD image
denoising adversarial attack method that maintains the Gaussian distribution.
Moreover, the model-driven image denoising BM3D shows some resistance in the
face of adversarial attacks.Comment: 12 pages, 15 figure
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