scholarly journals Denoising and Motion Artifact Removal Using Deformable Kernel Prediction Neural Network for Color-Intensified CMOS

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3891
Author(s):  
Zhenghao Han ◽  
Li Li ◽  
Weiqi Jin ◽  
Xia Wang ◽  
Gangcheng Jiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Noisy Data ◽  
Motion Artifact ◽  
Light Level ◽  
Motion Artifacts ◽  
Night Vision ◽  
Artifact Removal ◽  
Low Light ◽  
Color Imaging ◽  
Image Intensifiers

Image intensifiers are used internationally as advanced military night-vision devices. They have better imaging performance in low-light-level conditions than CMOS/CCD. The intensified CMOS (ICMOS) was developed to satisfy the digital demand of image intensifiers. In order to make the ICMOS capable of color imaging in low-light-level conditions, a liquid-crystal tunable filter based color imaging ICMOS was developed. Due to the time-division color imaging scheme, motion artifacts may be introduced when a moving target is in the scene. To solve this problem, a deformable kernel prediction neural network (DKPNN) is proposed for joint denoising and motion artifact removal, and a data generation method which generates images with color-channel motion artifacts is also proposed to train the DKPNN. The results show that, compared with other denoising methods, the proposed DKPNN performed better both on generated noisy data and on real noisy data. Therefore, the proposed DKPNN is more suitable for color ICMOS denoising and motion artifact removal. A new exploration was made for low-light-level color imaging schemes.

Test and analysis of the halo in low-light-level image intensifiers

2012 ◽  
Vol 10 (6) ◽  
pp. 060401-60403 ◽  
Author(s):  
Dongxu Cui Dongxu Cui ◽  
Ling Ren Ling Ren ◽  
Feng Shi Feng Shi ◽  
Jifang Shi Jifang Shi ◽  
Yunsheng Qian Yunsheng Qian ◽  
...  
Keyword(s):  
Light Level ◽  
Low Light ◽  
Image Intensifiers

A Comparison of Visual Acuity Performance between a Binocular Night Vision Goggle and Two Types of Monocular Night Vision Goggles

1980 ◽  
Vol 24 (1) ◽  
pp. 306-309
Author(s):  
Robert M. Waters ◽  
Larry W. Avery
Keyword(s):  
Visual Acuity ◽  
Depth Perception ◽  
Low Cost ◽  
Light Level ◽  
Night Vision ◽  
Low Light ◽  
Light Intensification

Two experiments were run comparing the operational binoculars AN/PVS-5 Night Vision Goggles with two monocular low cost night vision goggles using newer light intensification techniques. No decrement in performance was noted in visual acuity or depth perception with the monocular low cost goggles; an improved capability was noted with the new goggles in low light level conditions.

scholarly journals Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network

2020 ◽  
Vol 30 (11) ◽  
pp. 5923-5932
Author(s):  
M.-L. Kromrey ◽  
D. Tamada ◽  
H. Johno ◽  
S. Funayama ◽  
N. Nagata ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Arterial Phase ◽  
Artifact Reduction ◽  
Gadoxetate Disodium ◽  
Mr Images ◽  
Liver Mri ◽  
Lesion Conspicuity

Abstract Objectives To reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium–enhanced multi-arterial phase MRI of the liver. Methods This retrospective study included 192 patients (131 men, 68.7 ± 10.3 years) receiving gadoxetate disodium–enhanced liver MRI in 2017. Datasets were submitted to a newly developed filter (MARC), consisting of 7 convolutional layers, and trained on 14,190 cropped images generated from abdominal MR images. Motion artifact for training was simulated by adding periodic k-space domain noise to the images. Original and filtered images of pre-contrast and 6 arterial phases (7 image sets per patient resulting in 1344 sets in total) were evaluated regarding motion artifacts on a 4-point scale. Lesion conspicuity in original and filtered images was ranked by side-by-side comparison. Results Of the 1344 original image sets, motion artifact score was 2 in 597, 3 in 165, and 4 in 54 sets. MARC significantly improved image quality over all phases showing an average motion artifact score of 1.97 ± 0.72 compared to 2.53 ± 0.71 in original MR images (p < 0.001). MARC improved motion scores from 2 to 1 in 177/596 (29.65%), from 3 to 2 in 119/165 (72.12%), and from 4 to 3 in 34/54 sets (62.96%). Lesion conspicuity was significantly improved (p < 0.001) without removing anatomical details. Conclusions Motion artifacts and lesion conspicuity of gadoxetate disodium–enhanced arterial phase liver MRI were significantly improved by the MARC filter, especially in cases with substantial artifacts. This method can be of high clinical value in subjects with failing breath-hold in the scan. Key Points • This study presents a newly developed deep learning–based filter for artifact reduction using convolutional neural network (motion artifact reduction with convolutional neural network, MARC). • MARC significantly improved MR image quality after gadoxetate disodium administration by reducing motion artifacts, especially in cases with severely degraded images. • Postprocessing with MARC led to better lesion conspicuity without removing anatomical details.

Halo performance on low light level image intensifiers

2013 ◽  
Author(s):  
Dongxu Cui ◽  
Ling Ren ◽  
Benkang Chang ◽  
Feng Shi ◽  
Jifang Shi ◽  
...  
Keyword(s):  
Light Level ◽  
Low Light ◽  
Image Intensifiers

scholarly journals Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2957 ◽  
Author(s):  
Gihyoun Lee ◽  
Sang Jin ◽  
Jinung An
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Motion Artifact ◽  
Correction Method ◽  
Motion Artifacts ◽  
Functional Near Infrared Spectroscopy ◽  
Artifact Correction ◽  
Performance Evaluation Index

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.

Low light level CMOS sensor for night vision systems

2015 ◽  
Author(s):  
Elad Gross ◽  
Ran Ginat ◽  
Ofer Nesher
Keyword(s):  
Light Level ◽  
Night Vision ◽  
Vision Systems ◽  
Low Light ◽  
Cmos Sensor

Digital simulation for low-light-level night vision imaging system

2000 ◽  
Author(s):  
Tingzhu Bai ◽  
Na Li ◽  
Zhengfeng Zou ◽  
Hansheng Lu ◽  
Guangjian Yan
Keyword(s):  
Imaging System ◽  
Digital Simulation ◽  
Light Level ◽  
Night Vision ◽  
Low Light

Analysis on Spectrum Response and Visual Range of Low Light Level Night Vision System Under Laser Illuminate

2010 ◽  
Vol 37 (1) ◽  
pp. 312-315
Author(s):  
刘伟 Liu Wei ◽  
付江涛 Fu Jiangtao ◽  
常本康 Chang Benkang
Keyword(s):  
Vision System ◽  
Light Level ◽  
Night Vision ◽  
Low Light ◽  
Visual Range ◽  
Spectrum Response
Sign in / Sign up

Export Citation Format

Share Document