Results 51 to 60 of about 6,876,175 (322)
Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots [PDF]
Computer Vision and Pattern Recognition, 2022 Real noisy-clean pairs on a large scale are costly and difficult to obtain. Meanwhile, supervised denoisers trained on synthetic data perform poorly in practice.
Zejin Wang +3 more
semanticscholar +1 more source
Edge-aware image denoising algorithm
Journal of Algorithms & Computational Technology, 2018 The key of image denoising algorithms is to preserve the details of the original image while denoising the noise in the image. The existing algorithms use the external information to better preserve the details of the image, but the use of external ...
Xiangning Zhang, Yan Yang, Lening Lin
doaj +1 more source
Image Denoising Using Hybrid Transforms [PDF]
Engineering and Technology Journal, 2007 In this paper a new family of transformation for image denoising ispresented, Multiridgelet and Walidlet transforms, which have been proposedas alternatives to Discrete Wavelet and Multiwavelet transforms.Walidlet transform is an intelligent tool for ...
Walid Mahmoud, Raghad Jassim
doaj +1 more source
Semi-Supervised Learning-Based Image Denoising for Big Data
IEEE Access, 2020 In this paper, the research of image noise reduction based on semi-supervised learning is carried out, and the neural network is used to reduce the noise of the image, so as to achieve more stable and good image display ability.
Kun Zhang, Kai Chen
doaj +1 more source
SinDDM: A Single Image Denoising Diffusion Model [PDF]
International Conference on Machine Learning, 2022 Denoising diffusion models (DDMs) have led to staggering performance leaps in image generation, editing and restoration. However, existing DDMs use very large datasets for training. Here, we introduce a framework for training a DDM on a single image. Our
V. Kulikov +3 more
semanticscholar +1 more source
Heterogeneous Window Transformer for Image Denoising [PDF]
IEEE Transactions on Systems, Man, and Cybernetics: SystemsDeep networks can usually depend on extracting more structural information to improve denoising results. However, they may ignore correlation between pixels from an image to pursue better-denoising performance.
Chunwei Tian +4 more
semanticscholar +1 more source
Spatial-Spectral Transformer for Hyperspectral Image Denoising [PDF]
AAAI Conference on Artificial Intelligence, 2022 Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications. Unfortunately, though witnessing the development of deep learning in HSI denoising area, existing convolution-based methods face the trade-off ...
Miaoyu Li, Ying Fu, Yulun Zhang
semanticscholar +1 more source
Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain [PDF]
, 2013 In digital cameras and mobile phones, there is an ongoing trend to increase the image resolution, decrease the sensor size and to use lower exposure times. Because smaller sensors inherently lead to more noise and a worse spatial resolution, digital post-
Aelterman, Jan +4 more
core +1 more source
Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss [PDF]
IEEE Transactions on Medical Imaging, 2017 The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient.
Qingsong Yang +9 more
semanticscholar +1 more source
Dilated Deep Residual Network for Image Denoising [PDF]
, 2017 Variations of deep neural networks such as convolutional neural network (CNN) have been successfully applied to image denoising. The goal is to automatically learn a mapping from a noisy image to a clean image given training data consisting of pairs of ...
Hu, Kaoning +2 more
core +3 more sources