Noise Flow: Noise Modeling With Conditional Normalizing Flows

Abdelhamed, Abdelrahman; Brubaker, Marcus A.; Brown, Michael S.

doi:10.1109/iccv.2019.00326

Cited by 167 publications

(136 citation statements)

References 25 publications

Supporting

Mentioning

129

Contrasting

Order By: Relevance

“…It is worth noting that there exist many deep statistical models for other settings. When performing conditional AD, for example, one can use GAN [291], VAE [292], and normalizing flow [293] variants that perform conditional density estimation. Likewise, there exist many DGMs for virtually all data types, including time-series data [292], [294], text [295], [296], and graphs [297]- [299], all of which may potentially be used for AD.…”

Section: E Discussionmentioning

confidence: 99%

A Unifying Review of Deep and Shallow Anomaly Detection

et al. 2021

View full text Add to dashboard Cite

show abstract

Section: E Discussionmentioning

confidence: 99%

A Unifying Review of Deep and Shallow Anomaly Detection

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In [50], [51], [52], a novel training strategy without ground-truth is proposed. In [53], [54], [55], [56], real noise synthesis technique is proposed to handle real digital photographs. However, from a Bayesian perspective, the denoiser for plug-and-play IR should be a Gaussian denoiser.…”

Section: Learning Deep Cnn Denoiser Priormentioning

confidence: 99%

Plug-and-Play Image Restoration With Deep Denoiser Prior

Zhang¹,

Li²,

Zuo

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

586

331

View full text Add to dashboard Cite

Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems. Such a property induces considerable advantages for plug-and-play image restoration (e.g., integrating the flexibility of model-based method and effectiveness of learning-based methods) when the denoiser is discriminatively learned via deep convolutional neural network (CNN) with large modeling capacity. However, while deeper and larger CNN models are rapidly gaining popularity, existing plug-and-play image restoration hinders its performance due to the lack of suitable denoiser prior. In order to push the limits of plug-and-play image restoration, we set up a benchmark deep denoiser prior by training a highly flexible and effective CNN denoiser. We then plug the deep denoiser prior as a modular part into a half quadratic splitting based iterative algorithm to solve various image restoration problems. We, meanwhile, provide a thorough analysis of parameter setting, intermediate results and empirical convergence to better understand the working mechanism. Experimental results on three representative image restoration tasks, including deblurring, super-resolution and demosaicing, demonstrate that the proposed plug-and-play image restoration with deep denoiser prior not only significantly outperforms other state-of-the-art model-based methods but also achieves competitive or even superior performance against state-of-the-art learning-based methods. The source code is available at https://github.com/cszn/DPIR.

show abstract

“…For these applications, several approaches have been proposed, from the most commonly used Poissonian–Gaussian noise model to noise models based on conditional normalizing flow architectures. These are used for modifying existing data, either captured or synthetic, into training sets suited for the problem at hand [BHLH19, ABB19]. For the same purposes, Lehtinen et al .…”

Section: Image Synthesis Methods Overviewmentioning

confidence: 99%

A Survey of Image Synthesis Methods for Visual Machine Learning

Tsirikoglou

Eilertsen

Unger

2020

Computer Graphics Forum

View full text Add to dashboard Cite

Image synthesis designed for machine learning applications provides the means to efficiently generate large quantities of training data while controlling the generation process to provide the best distribution and content variety. With the demands of deep learning applications, synthetic data have the potential of becoming a vital component in the training pipeline. Over the last decade, a wide variety of training data generation methods has been demonstrated. The potential of future development calls to bring these together for comparison and categorization. This survey provides a comprehensive list of the existing image synthesis methods for visual machine learning. These are categorized in the context of image generation, using a taxonomy based on modelling and rendering, while a classification is also made concerning the computer vision applications they are used. We focus on the computer graphics aspects of the methods, to promote future image generation for machine learning. Finally, each method is assessed in terms of quality and reported performance, providing a hint on its expected learning potential. The report serves as a comprehensive reference, targeting both groups of the applications and data development sides. A list of all methods and papers reviewed herein can be found at https://computergraphics.on.liu.se/image_synthesis_methods_for_visual_machine_learning/.

show abstract

Noise Flow: Noise Modeling With Conditional Normalizing Flows

Cited by 167 publications

References 25 publications

A Unifying Review of Deep and Shallow Anomaly Detection

A Unifying Review of Deep and Shallow Anomaly Detection

Plug-and-Play Image Restoration With Deep Denoiser Prior

A Survey of Image Synthesis Methods for Visual Machine Learning

Contact Info

Product

Resources

About