Results 231 to 240 of about 17,318 (265)

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

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.
openaire   +1 more source

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
openaire   +1 more source

Brightening and denoising lowlight images

2015 10th International Conference on Information, Communications and Signal Processing (ICICS), 2015
This paper aims to improve the quality of images in low light conditions. After nonlinear intensity stretching, the enlarged mixed noise is reduced using a newly proposed low rank denoising algorithm. By similar patch stacking, the established noisy matrices are assumed to be composed of low-rank noise free image, sparse random impulse noise and zero ...
Xin-Wei Yang, Xiang-Bo Lin
openaire   +1 more source

Neural Adaptive Image Denoiser

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
We propose a novel neural network-based adaptive image denoiser, dubbased as Neural AIDE. Unlike other neural network-based denoisers, which typically apply supervised training to learn a mapping from a noisy patch to a clean patch, we formulate to train a neural network to learn context-based affine mappings that get applied to each noisy pixel.
Sungmin Cha, Taesup Moon
openaire   +1 more source

Denoising and inpainting SONAR images

2015 38th International Conference on Telecommunications and Signal Processing (TSP), 2015
The basic principle of SONAR is to use sound to detect or locate objects, typically in the ocean. During acquisition, some pixels of SONAR images could be lost. The recovery of lost or damaged data can be done by using an inpainting algorithm. But, SONAR images are perturbed by speckle noise, so speckle reduction filters should be used to improve the ...
Cristina Stolojescu-Crisan, Alexandru Isar   +1 more
openaire   +1 more source

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

Deep Boosting for Image Denoising

2018
Boosting is a classic algorithm which has been successfully applied to diverse computer vision tasks. In the scenario of image denoising, however, the existing boosting algorithms are surpassed by the emerging learning-based models. In this paper, we propose a novel deep boosting framework (DBF) for denoising, which integrates several convolutional ...
Chang Chen 0004, Zhiwei Xiong, Xinmei Tian 0001, Feng Wu 0001   +3 more
openaire   +1 more source