NoiseTransfer: Image Noise Generation with Contrastive Embeddings

• URL: http://arxiv.org/abs/2301.13554v1
• Date: Tue, 31 Jan 2023 11:09:15 GMT
• Title: NoiseTransfer: Image Noise Generation with Contrastive Embeddings
• Authors: Seunghwan Lee and Tae Hyun Kim
• Abstract summary: We propose a new generative model that can synthesize noisy images with multiple different noise distributions. We adopt recent contrastive learning to learn distinguishable latent features of the noise. Our model can generate new noisy images by transferring the noise characteristics solely from a single reference noisy image.
• Score: 9.322843611215486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep image denoising networks have achieved impressive success with the help of a considerably large number of synthetic train datasets. However, real-world denoising is a still challenging problem due to the dissimilarity between distributions of real and synthetic noisy datasets. Although several real-world noisy datasets have been presented, the number of train datasets (i.e., pairs of clean and real noisy images) is limited, and acquiring more real noise datasets is laborious and expensive. To mitigate this problem, numerous attempts to simulate real noise models using generative models have been studied. Nevertheless, previous works had to train multiple networks to handle multiple different noise distributions. By contrast, we propose a new generative model that can synthesize noisy images with multiple different noise distributions. Specifically, we adopt recent contrastive learning to learn distinguishable latent features of the noise. Moreover, our model can generate new noisy images by transferring the noise characteristics solely from a single reference noisy image. We demonstrate the accuracy and the effectiveness of our noise model for both known and unknown noise removal.

Related papers

• Realistic Noise Synthesis with Diffusion Models [68.48859665320828]
  Deep image denoising models often rely on large amount of training data for the high quality performance. We propose a novel method that synthesizes realistic noise using diffusion models, namely Realistic Noise Synthesize Diffusor (RNSD) RNSD can incorporate guided multiscale content, such as more realistic noise with spatial correlations can be generated at multiple frequencies.
  arXiv  Detail & Related papers  (2023-05-23T12:56:01Z)
• Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images [35.29066692454865]
  This paper proposes a framework for training a noise model and a denoiser simultaneously. It relies on pairs of noisy images rather than noisy/clean paired image data. The trained denoiser is shown to significantly improve upon both supervised and weakly supervised baseline denoising approaches.
  arXiv  Detail & Related papers  (2022-06-02T15:31:40Z)
• 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)
• Estimating Fine-Grained Noise Model via Contrastive Learning [11.626812663592416]
  We propose an innovative noise model estimation and noise synthesis pipeline for realistic noisy image generation. Our model learns a noise estimation model with fine-grained statistical noise model in a contrastive manner. By calibrating noise models of several sensors, our model can be extended to predict other cameras.
  arXiv  Detail & Related papers  (2022-04-03T02:35:01Z)
• C2N: Practical Generative Noise Modeling for Real-World Denoising [53.96391787869974]
  We introduce a Clean-to-Noisy image generation framework, namely C2N, to imitate complex real-world noise without using paired examples. We construct the noise generator in C2N accordingly with each component of real-world noise characteristics to express a wide range of noise accurately.
  arXiv  Detail & Related papers  (2022-02-19T05:53:46Z)
• Physics-based Noise Modeling for Extreme Low-light Photography [63.65570751728917]
  We study the noise statistics in the imaging pipeline of CMOS photosensors. We formulate a comprehensive noise model that can accurately characterize the real noise structures. Our noise model can be used to synthesize realistic training data for learning-based low-light denoising algorithms.
  arXiv  Detail & Related papers  (2021-08-04T16:36:29Z)
• 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)
• Adaptive noise imitation for image denoising [58.21456707617451]
  We develop a new textbfadaptive noise imitation (ADANI) algorithm that can synthesize noisy data from naturally noisy images. To produce realistic noise, a noise generator takes unpaired noisy/clean images as input, where the noisy image is a guide for noise generation. Coupling the noisy data output from ADANI with the corresponding ground-truth, a denoising CNN is then trained in a fully-supervised manner.
  arXiv  Detail & Related papers  (2020-11-30T02:49:36Z)

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.