Results 61 to 70 of about 39,050 (216)
Medical Image Denoising Using Convolutional Denoising Autoencoders [PDF]
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016 To appear: 6 pages, paper to be published at the Fourth Workshop on Data Mining in Biomedical Informatics and Healthcare at ICDM ...
openaire +3 more sources
Gaussian Priors for Image Denoising [PDF]
, 2018 This chapter is dedicated to the study of Gaussian priors for patch-based image denoising. In the last 12 years, patch priors have been widely used for image restoration. In a Bayesian framework, such priors on patches can be used for instance to estimate a clean patch from its noisy version, via classical estimators such as the conditional expectation
Delon, Julie, Houdard, Antoine
openaire +3 more sources
Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network
Sensors, 2020 A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array.
Yunjin Park +3 more
doaj +1 more source
IET Image ProcessingImage denoising aims to remove noise from images and improve the quality of images. However, most image denoising methods heavily rely on pairwise training strategies and strict prior knowledge about image structure or noise distribution.
Jing Wu, Ruilin Xie, Hao Wu, Guowu Yuan
doaj +1 more source
TELKOMNIKA Indonesian Journal of Electrical Engineering, 2014 A Mixed Window Shrink and BayesShrink Image Denoising Algorithm Based on Curve let Transform is proposed in this paper. Curve let transform is effective in presenting line and surface property of image. In the proposed algorithm, Curvelet transform is employed for the first stage, then according the theory of image demising method based on Wavelet ...
Jia Meng, Zhang Ye
openaire +2 more sources
Iterative denoising of ghost imaging
Optics Express, 2014 We present a new technique to denoise ghost imaging (GI) in which conventional intensity correlation GI and an iteration process have been combined to give an accurate estimate of the actual noise affecting image quality. The blurring influence of the speckle areas in the beam is reduced in the iteration by setting a threshold. It is shown that with an
Long-Zhen Li +6 more
openaire +3 more sources
Abstract and Applied Analysis, 2014 The interest in using fractional mask operators based on fractional calculus operators has grown for image denoising. Denoising is one of the most fundamental image restoration problems in computer vision and image processing.
Hamid A. Jalab
doaj +1 more source
Spatially adaptive wavelet thresholding with context modeling for image denoising [PDF]
, 2000 Sheng Chang, Bin Yu, Martin Vetterli
openalex +1 more source
Equivariant Denoisers for Image Restoration
One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Moreover, typical image distributions are invariant to some set of transformations, such as rotations or flips.
Renaud, Marien +2 more
openaire +3 more sources
Local adaptive transform based image denoising with varying window size [PDF]
, 2002 Hakan Öktem +3 more
openalex +1 more source