A two-step adc architecture for high dynamic range and high sensitivity image sensor

2020 ◽  
Author(s):  
Shengyou Zhong ◽  
Wenbiao Mao ◽  
Mei Zou ◽  
Jiqing Zhang ◽  
Nan Chen ◽  
...  
Keyword(s):  
Dynamic Range ◽  
High Sensitivity ◽  
High Dynamic Range ◽  
Image Sensor ◽  
High Dynamic

scholarly journals An ultra-small, multi-point, and multi-color photo-detection system with high sensitivity and high dynamic range

Lab on a Chip ◽  
2017 ◽  
Vol 17 (24) ◽  
pp. 4231-4242 ◽  
Author(s):  
Takashi Anazawa ◽  
Motohiro Yamazaki
Keyword(s):  
Dynamic Range ◽  
Detection System ◽  
High Sensitivity ◽  
High Dynamic Range ◽  
Image Sensor ◽  
High Dynamic ◽  
Photo Detection

Fluorescence from four emission points is collimated by four lenses, split into four-color fluxes by four dichroic mirrors, and directly input into the image sensor.

A High Dynamic Range CMOS Image Sensor With Dual-Exposure Charge Subtraction Scheme

2015 ◽  
Vol 15 (2) ◽  
pp. 661-662 ◽  
Author(s):  
Xinyuan Qian ◽  
Hang Yu ◽  
Shoushun Chen ◽  
Kay Soon Low
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Subtraction Scheme ◽  
High Dynamic

scholarly journals Electronic Optical Sky Surveys

1998 ◽  
Vol 179 ◽  
pp. 49-55
Author(s):  
T.A. McKay
Keyword(s):  
Dynamic Range ◽  
Large Fraction ◽  
High Sensitivity ◽  
High Dynamic Range ◽  
Low Noise ◽  
Computer Hardware ◽  
Optical Detectors ◽  
Small Fields ◽  
Technical Advances ◽  
High Dynamic

The introduction of of Charge Coupled Devices (CCDs) in the middle 1970s provided astronomy with nearly perfect (linear, high-sensitivity, low-noise, high dynamic-range, digital) optical detectors. Unfortunately, restrictions imposed by CCD production and cost has typically limited their use to observations of relatively small fields. Recently a combination of technical advances have made practical the application of CCDs to survey science. CCD mosaic cameras, which help overcome the size restrictions imposed by CCD manufacture, allow electronic access to a larger fraction of the available focal plane. Multi-fiber spectrographs, which couple the low-noise, high QE performance of CCDs with the ability to observe spectra for many objects at once, have improved the spectroscopic efficiency of telescopes by factors approaching half a million. An improved understanding of image distortion gives us telescopes on which we expect sub-arcsecond images a large fraction of the time. Finally, and perhaps most important, the performance of computer hardware continues to advance, to the point where analysis of multi-terabyte datasets, while still daunting, is at least conceivable.

scholarly journals Anti-Shake HDR Imaging Using RAW Image Data

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 213 ◽  
Author(s):  
Yan Liu ◽  
Bingxue Lv ◽  
Wei Huang ◽  
Baohua Jin ◽  
Canlin Li
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Image Sensor ◽  
Image Sequences ◽  
Reference Image ◽  
Imaging Method ◽  
Camera Motion ◽  
Range Image ◽  
Real Scene ◽  
High Dynamic

Camera shaking and object movement can cause the output images to suffer from blurring, noise, and other artifacts, leading to poor image quality and low dynamic range. Raw images contain minimally processed data from the image sensor compared with JPEG images. In this paper, an anti-shake high-dynamic-range imaging method is presented. This method is more robust to camera motion than previous techniques. An algorithm based on information entropy is employed to choose a reference image from the raw image sequence. To further improve the robustness of the proposed method, the Oriented FAST and Rotated BRIEF (ORB) algorithm is adopted to register the inputs, and a simple Laplacian pyramid fusion method is implanted to generate the high-dynamic-range image. Additionally, a large dataset with 435 various exposure image sequences is collected, which includes the corresponding JPEG image sequences to test the effectiveness of the proposed method. The experimental results illustrate that the proposed method achieves better performance in terms of anti-shake ability and preserves more details for real scene images than traditional algorithms. Furthermore, the proposed method is suitable for extreme-exposure image pairs, which can be applied to binocular vision systems to acquire high-quality real scene images, and has a lower algorithm complexity than deep learning-based fusion methods.

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