Dual Adversarial Network: Toward Real-world Noise Removal and Noise
Generation
- URL: http://arxiv.org/abs/2007.05946v1
- Date: Sun, 12 Jul 2020 09:16:06 GMT
- Title: Dual Adversarial Network: Toward Real-world Noise Removal and Noise
Generation
- Authors: Zongsheng Yue, Qian Zhao, Lei Zhang, Deyu Meng
- Abstract summary: Real-world image noise removal is a long-standing yet very challenging task in computer vision.
We propose a novel unified framework to deal with the noise removal and noise generation tasks.
Our method learns the joint distribution of the clean-noisy image pairs.
- Score: 52.75909685172843
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Real-world image noise removal is a long-standing yet very challenging task
in computer vision. The success of deep neural network in denoising stimulates
the research of noise generation, aiming at synthesizing more clean-noisy image
pairs to facilitate the training of deep denoisers. In this work, we propose a
novel unified framework to simultaneously deal with the noise removal and noise
generation tasks. Instead of only inferring the posteriori distribution of the
latent clean image conditioned on the observed noisy image in traditional MAP
framework, our proposed method learns the joint distribution of the clean-noisy
image pairs. Specifically, we approximate the joint distribution with two
different factorized forms, which can be formulated as a denoiser mapping the
noisy image to the clean one and a generator mapping the clean image to the
noisy one. The learned joint distribution implicitly contains all the
information between the noisy and clean images, avoiding the necessity of
manually designing the image priors and noise assumptions as traditional.
Besides, the performance of our denoiser can be further improved by augmenting
the original training dataset with the learned generator. Moreover, we propose
two metrics to assess the quality of the generated noisy image, for which, to
the best of our knowledge, such metrics are firstly proposed along this
research line. Extensive experiments have been conducted to demonstrate the
superiority of our method over the state-of-the-arts both in the real noise
removal and generation tasks. The training and testing code is available at
https://github.com/zsyOAOA/DANet.
Related papers
- Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising [19.08732222562782]
Supervised deep learning has become the method of choice for image denoising.
We show that, contrary to popular belief, denoising networks specialized in the removal of Gaussian noise can be efficiently leveraged in favor of real-world image denoising.
arXiv Detail & Related papers (2024-07-24T16:23:46Z) - Multi-view Self-supervised Disentanglement for General Image Denoising [22.28610604896056]
We propose to learn to disentangle the noisy image, under the intuitive assumption that different corrupted versions of the same clean image share a common latent space.
A self-supervised learning framework is proposed to achieve the goal, without looking at the latent clean image.
By taking two different corrupted versions of the same image as input, the proposed Multi-view Self-supervised Disentanglement (MeD) approach learns to disentangle the latent clean features from the corruptions and recover the clean image consequently.
arXiv Detail & Related papers (2023-09-10T14:54:44Z) - Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware
Adversarial Training [50.018580462619425]
We propose a novel framework, namely Pixel-level Noise-aware Generative Adrial Network (PNGAN)
PNGAN employs a pre-trained real denoiser to map the fake and real noisy images into a nearly noise-free solution space.
For better noise fitting, we present an efficient architecture Simple Multi-versa-scale Network (SMNet) as the generator.
arXiv Detail & Related papers (2022-04-06T14:09:02Z) - IDR: Self-Supervised Image Denoising via Iterative Data Refinement [66.5510583957863]
We present a practical unsupervised image denoising method to achieve state-of-the-art denoising performance.
Our method only requires single noisy images and a noise model, which is easily accessible in practical raw image denoising.
To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes.
arXiv Detail & Related papers (2021-11-29T07:22:53Z) - Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images [98.82804259905478]
We present Neighbor2Neighbor to train an effective image denoising model with only noisy images.
In detail, input and target used to train a network are images sub-sampled from the same noisy image.
A denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance.
arXiv Detail & Related papers (2021-01-08T02:03:25Z) - Unpaired Learning of Deep Image Denoising [80.34135728841382]
This paper presents a two-stage scheme by incorporating self-supervised learning and knowledge distillation.
For self-supervised learning, we suggest a dilated blind-spot network (D-BSN) to learn denoising solely from real noisy images.
Experiments show that our unpaired learning method performs favorably on both synthetic noisy images and real-world noisy photographs.
arXiv Detail & Related papers (2020-08-31T16:22:40Z) - Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
Blind image denoising is an important yet very challenging problem in computer vision.
We propose a new variational inference method, which integrates both noise estimation and image denoising.
arXiv Detail & Related papers (2019-08-29T15:54:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.