Results 71 to 80 of about 158,938 (168)

A unified convolutional beamformer for simultaneous denoising and dereverberation

, 2019
This paper proposes a method for estimating a convolutional beamformer that can perform denoising and dereverberation simultaneously in an optimal way.
Kinoshita, Keisuke, Nakatani, Tomohiro
core   +1 more source

A New Denoising Method for Underwater Acoustic Signal

IEEE Access, 2020
In recent years, the rapid development of marine science has put forward higher and higher requirements for the processing of ship-radiated noise signal.
Hong Yang, Lulu Li, Guohui Li
doaj   +1 more source

Deep Learning Model to Denoise Luminescence Images of Silicon Solar Cells

Advanced Science, 2023
Luminescence imaging is widely used to identify spatial defects and extract key electrical parameters of photovoltaic devices. To reliably identify defects, high‐quality images are desirable; however, acquiring such images implies a higher cost or lower ...
Grace Liu, Priya Dwivedi, Thorsten Trupke, Ziv Hameiri   +3 more
doaj   +1 more source

When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

, 2018
Conventionally, image denoising and high-level vision tasks are handled separately in computer vision. In this paper, we cope with the two jointly and explore the mutual influence between them.
Huang, Thomas S., Liu, Ding, Liu, Xianming, Wang, Zhangyang, Wen, Bihan   +4 more
core   +1 more source

Segmentation‐enhanced gamma spectrum denoising based on deep learning

IET Communications
Gamma spectrum denoising can reduce the adverse effects of statistical fluctuations of radioactivity, gamma ray scattering, and electronic noise on the measured gamma spectrum.
Xiangqun Lu, Hongzhi Zheng, Yaqiong Liu, Hongxing Li, Qingyun Zhou, Tao Li, Hongguang Yang   +6 more
doaj   +1 more source

Low-Dose CT Image Denoising Using a Generative Adversarial Network With a Hybrid Loss Function for Noise Learning

IEEE Access, 2020
Potential risk of X-ray radiation from computed tomography (CT) has been a concern of the public. However, simply decreasing the dose will degrade quality of the CT images and compromise diagnostic performance.
Yinjin Ma, Biao Wei, Peng Feng, Peng He, Xiaodong Guo, Ge Wang   +5 more
doaj   +1 more source

A fractional integral method inverse distance weight-based for denoising depth images

AIP Advances
Denoising algorithms for obtaining the effective data of depth images affected by random noise mainly focus on the processing of gray images. These algorithms are not distinct from traditional image-processing methods, and there is no way to evaluate the
Da Xie, Tingsheng Huang, Chunyang Wang, Wenqian Qiu, Xuelian Liu, Yanqing Yu   +5 more
doaj   +1 more source

Self-supervised PET Denoising

Nuclear Medicine and Molecular Imaging, 2020
Early deep-learning-based image denoising techniques mainly focused on a fully supervised model that learns how to generate a clean image from the noisy input (noise2clean: N2C). The aim of this study is to explore the feasibility of the self-supervised methods (noise2noise: N2N and noiser2noise: Nr2N) for PET image denoising based on the measured PET ...
Si Young Yie, Seung Kwan Kang, Donghwi Hwang, Jae Sung Lee   +3 more
openaire   +3 more sources

Supervised Neural Discrete Universal Denoiser for Adaptive Denoising

, 2021
Preprint
Cha, Sungmin, Min, Seonwoo, Yoon, Sungroh, Moon, Taesup   +3 more
openaire   +2 more sources

Denosing Using Wavelets and Projections onto the L1-Ball [PDF]

, 2014
Both wavelet denoising and denosing methods using the concept of sparsity are based on soft-thresholding. In sparsity based denoising methods, it is assumed that the original signal is sparse in some transform domains such as the wavelet domain and the ...
Cetin, A. Enis, Tofighi, Mohammad
core   +1 more source