Digital image processing at Los Alamos Scientific Laboratory

Hunt, B. R.; Janney, D. H.

doi:10.1109/mc.1974.6323531

Cited by 10 publications

(2 citation statements)

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“…Therefore it is impossible to observe the image content clearly and the noisy image may effect on the high-level vision tasks. Accordingly, it is an essential step to remove the noise from the observed image in various image processing and computer vision tasks [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…In the past decades, a large amount of denoising methods were proposed to recover the clean image from the noisy observation. And a gorgeous image denoising algorithm is supposed to have the following features: (1) It is flexible enough to recover the clean images with unknown noise. In other words, different noisy images can be recovered with a single model.…”

Section: Introductionmentioning

confidence: 99%

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A new blind image denoising method based on asymmetric generative adversarial network

Wang

Chang

Zhao

2021

IET Image Processing

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Image denoising is a classical topic in computer vision. In recent years, with the development of deep learning, image denoising methods based on discriminative learning have received more attention. In this paper, a new blind image denoising method based on the asymmetric generative adversarial network (ID‐AGAN) is proposed. In the new method, the adversarial learning is used to optimise the high‐dimensional image information denoising, so as to balance the noise removal and detail retention. In order to overcome the unstability of the GAN training and improve the discriminative ability of the discriminating model, an image downsampling layer is added between the generating model and the discriminating model. Moreover, a multi‐scale feature downsampling layer is utilised to extract the feature of the entire image and reducing the effect of noise on training images. Extensive experiments are conducted to verify the performance of the ID‐AGAN algorithm. The results demonstrate that authors' method has high performance and flexibility.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%