Performance characterization of low light level color imaging sensors

2021 ◽  
Author(s):  
Carolynn A. Moore ◽  
Alec Brown ◽  
Christian Sias ◽  
Timothy R. Robinson ◽  
Toomas H. Allik
Keyword(s):  
Light Level ◽  
Low Light ◽  
Color Imaging ◽  
Performance Characterization ◽  
Imaging Sensors

scholarly journals Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction Network

Photonics ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 321
Author(s):  
Bowen Wang ◽  
Yan Zou ◽  
Linfei Zhang ◽  
Yan Hu ◽  
Hao Yan ◽  
...  
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Light Level ◽  
Superior Performance ◽  
Wide Field ◽  
Low Light ◽  
Color Mapping ◽  
Multi Scale ◽  
Imaging Sensors

Wide field-of-view (FOV) and high-resolution (HR) imaging are essential to many applications where high-content image acquisition is necessary. However, due to the insufficient spatial sampling of the image detector and the trade-off between pixel size and photosensitivity, the ability of current imaging sensors to obtain high spatial resolution is limited, especially under low-light-level (LLL) imaging conditions. To solve these problems, we propose a multi-scale feature extraction (MSFE) network to realize pixel-super-resolved LLL imaging. In order to perform data fusion and information extraction for low resolution (LR) images, the network extracts high-frequency detail information from different dimensions by combining the channel attention mechanism module and skip connection module. In this way, the calculation of the high-frequency components can receive greater attention. Compared with other networks, the peak signal-to-noise ratio of the reconstructed image was increased by 1.67 dB. Extensions of the MSFE network are investigated for scene-based color mapping of the gray image. Most of the color information could be recovered, and the similarity with the real image reached 0.728. The qualitative and quantitative experimental results show that the proposed method achieved superior performance in image fidelity and detail enhancement over the state-of-the-art.

Characterization of low-light-level cameras for digitized video microscopy

1990 ◽  
Vol 160 (2) ◽  
pp. 141-159 ◽  
Author(s):  
Tsong-Tseh Tsay ◽  
Richard Inman ◽  
Barnaby Wray ◽  
Brian Herman ◽  
Ken Jacobson
Keyword(s):  
Light Level ◽  
Video Microscopy ◽  
Low Light

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.

scholarly journals Ultrahigh-Speed Color Imaging with Single-Pixel Detectors at Low Light Level

2019 ◽  
Vol 12 (3) ◽  
Author(s):  
Weigang Zhao ◽  
Hui Chen ◽  
Yuan Yuan ◽  
Huaibin Zheng ◽  
Jianbin Liu ◽  
...  
Keyword(s):  
Light Level ◽  
Low Light ◽  
Color Imaging ◽  
Pixel Detectors ◽  
Single Pixel

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Low-light-level three-dimensional video microscope with long working distance objective lenses

1994 ◽  
Vol 52 ◽  
pp. 226-227
Author(s):  
W. Lin ◽  
J. Gregorio ◽  
T.J. Holmes ◽  
D. H. Szarowski ◽  
J.N. Turner
Keyword(s):  
Three Dimensional ◽  
Light Level ◽  
Stereo Pair ◽  
Light Illumination ◽  
Initial Image ◽  
Low Light ◽  
Image Recording ◽  
Depth Discrimination ◽  
Video Microscope ◽  
Working Distance

A low-light level video microscope with long working distance objective lenses has been built as part of our integrated three-dimensional (3-D) light microscopy workstation (Fig. 1). It allows the observation of living specimens under sufficiently low light illumination that no significant photobleaching or alternation of specimen physiology is produced. The improved image quality, depth discrimination and 3-D reconstruction provides a versatile intermediate resolution system that replaces the commonly used dissection microscope for initial image recording and positioning of microelectrodes for neurobiology. A 3-D image is displayed on-line to guide the execution of complex experiments. An image composed of 40 optical sections requires 7 minutes to process and display a stereo pair.The low-light level video microscope utilizes long working distance objective lenses from Mitutoyo (10X, 0.28NA, 37 mm working distance; 20X, 0.42NA, 20 mm working distance; 50X, 0.42NA, 20 mm working distance). They provide enough working distance to allow the placement of microelectrodes in the specimen.

Sign in / Sign up

Export Citation Format

Share Document