Results 221 to 230 of about 11,782,843 (259)

Multiscale Image Blind Denoising

IEEE Transactions on Image Processing, 2015
Arguably several thousands papers are dedicated to image denoising. Most papers assume a fixed noise model, mainly white Gaussian or Poissonian. This assumption is only valid for raw images. Yet, in most images handled by the public and even by scientists, the noise model is imperfectly known or unknown.
Lebrun, Marc, Colom, Miguel, Morel, Jean-Michel   +2 more
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Image denoising with complex ridgelets

Pattern Recognition, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chen, Guangyi, Kégl, Balázs
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PageRank Image Denoising

2010
We present a novel probabilistic algorithm for image noise removal. The algorithm is inspired by the Google PageRank algorithm for ranking hypertextual world wide web documents and based upon considering the topological structure of the photometric similarity between image pixels. We provide computationally efficient strategies for obtaining a solution
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Complexity-regularized image denoising

IEEE Transactions on Image Processing, 2001
Summary: We study a new approach to image denoising based on complexity regularization. This technique presents a flexible alternative to the more conventional \(l^2\), \(l^1\), and Besov regularization methods. Different complexity measures are considered, in particular those induced by state-of-the-art image coders.
Juan Liu, Pierre Moulin
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Multiwedgelets in Image Denoising

2013
In this paper the definition of a multiwedgelet is introduced. The multiwedgelet is defined as a vector of wedgelets. In order to use a multiwedgelet in image approximation its visualization and computation methods are also proposed. The application of multiwedgelets in image denoising is presented, as well.
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Curvelet image denoising of mammogram images

International Journal of Medical Engineering and Informatics, 2013
Mammography, the most commonly used diagnostic technique is used for early detection of breast cancer. As mammograms are low contrast and noisy images, it is essential to reduce noise while preserving fine details and edges. In order to obtain efficient diagnosis, a constructive analysis curvelet is used to provide optimal sparse representation of ...
Malar Elangeeran, A. Kandaswamy, S. S. Kirthana, D. Nivedhitha, M. Gauthaam   +4 more
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An image topic model for image denoising

Neurocomputing, 2015
Abstract Topic model is a powerful tool for the basic document or image processing tasks. In this study we introduce a novel image topic model, called Latent Patch Model (LPM), which is a generative Bayesian model and assumes that the image and pixels are connected by a latent patch layer.
Bo Fu 0001, Weiwei Li, You-Ping Fu, Chuan-Ming Song 0001   +3 more
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Iterative denoising of sparse images

2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2016
The paper examines an application of the gradient-based algorithm to image denoising with noise values being in the range of the available (non-noisy) pixel values. The analyzed image is considered to be sparse in the 2D-DCT domain. The presented algorithm is a generalization of the previous results on denoising images when the noisy pixels can be ...
Isidora Stankovic, Irena Orovic, Srdjan Stankovic, Milos Dakovic   +3 more
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A Denoising Framework for Image Caption

2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 2019
Image caption is one of the hottest research topics at the moment in the image processing field. However, most image caption models based on the Encoder-Decoder framework cannot accurately find the alignment relationship between objects in the image and objects in the text, resulting in an inaccurate description.
Yulong Zhang, Yuxin Ding, Rui Wu, Fuxing Xue   +3 more
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Sodium Image Denoising Based on a Convolutional Denoising Autoencoder

2019
Sodium Magnetic Resonance Imaging (sodium MRI) is an imaging modality that has gained momentum over the past decade, because of its potential ability to become a biomarker for several diseases, ranging from cancer to neurodegenerative pathologies, along with monitoring of tissues metabolism.
Simon Koppers, Edouard Coussoux, Sandro Romanzetti, Kathrin Reetz, Dorit Merhof   +4 more
openaire   +1 more source