Results 11 to 20 of about 259,715 (355)
Adversarial Gaussian Denoiser for Multiple-Level Image Denoising [PDF]
Sensors, 2021 Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian ...
Aamir Khan +4 more
doaj +4 more sources
IEEE Transactions on Image Processing, 2007 A new threshold rule for the estimation of a deterministic image immersed in noise is proposed. The full estimation procedure is based on a separable wavelet decomposition of the observed image, and the estimation is improved by introducing the new ...
Olhede, SC
core +6 more sources
Medical image denoising using convolutional denoising autoencoders [PDF]
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016 Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances.
Gondara, Lovedeep
core +3 more sources
Noise Invalidation Denoising [PDF]
IEEE Transactions on Signal Processing, 2010 A denoising technique based on noise invalidation is proposed. The adaptive approach derives a noise signature from the noise order statistics and utilizes the signature to denoise the data. The novelty of this approach is in presenting a general-purpose
Beheshti, Soosan +3 more
core +3 more sources
Denoising and contrast constancy
Vision Research, 2004 Contrast constancy is the ability to perceive object contrast independent of size or spatial frequency, even though these affect both retinal contrast and detectability. Like other perceptual constancies, it is evidence that the visual system infers the stable properties of objects from the changing properties of retinal images.
William McIlhagga
openalex +6 more sources
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing, 2000 Shih-Fu Chang, Bin Yu, Martin Vetterli
openalex +3 more sources
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection [PDF]
International Conference on Learning Representations, 2022 We present DINO (\textbf{D}ETR with \textbf{I}mproved de\textbf{N}oising anch\textbf{O}r boxes), a state-of-the-art end-to-end object detector. % in this paper.
Hao Zhang +7 more
semanticscholar +1 more source
RePaint: Inpainting using Denoising Diffusion Probabilistic Models [PDF]
Computer Vision and Pattern Recognition, 2022 Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask ...
Andreas Lugmayr +5 more
semanticscholar +1 more source
Denoising Diffusion Models for Plug-and-Play Image Restoration [PDF]
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023 Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser as the implicit image prior.
Yuanzhi Zhu +6 more
semanticscholar +1 more source
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising [PDF]
Computer Vision and Pattern Recognition, 2022 We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods.
Feng Li +5 more
semanticscholar +1 more source