scholarly journals Architectures for High Dynamic Range, High Speed Image Sensor Readout Circuits

2007 ◽  
pp. 1-23 ◽  
Author(s):  
Sam Kavusi ◽  
Kunal Ghosh ◽  
Abbas Gamal
Keyword(s):  
High Speed ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Image Sensor ◽  
Readout Circuits ◽  
High Dynamic

A High Dynamic Range CMOS Image Sensor for Scientific Imaging Applications

2009 ◽  
Vol 9 (10) ◽  
pp. 1209-1218 ◽  
Author(s):  
Jian Guo ◽  
Sameer Sonkusale
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
High Dynamic

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 High Speed and High Dynamic Range Video with an Event Camera

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Henri Rebecq ◽  
Rene Ranftl ◽  
Vladlen Koltun ◽  
Davide Scaramuzza
Keyword(s):  
High Speed ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Event Camera ◽  
High Dynamic ◽  
High Dynamic Range Video

High-Speed, High Dynamic-Range Optical Sensor Arrays

2009 ◽  
Vol 56 (3) ◽  
pp. 1069-1075 ◽  
Author(s):  
Stuart Kleinfelder ◽  
Shiuh-Hua Wood Chiang ◽  
Wei Huang ◽  
Ashish Shah ◽  
Kris Kwiatkowski
Keyword(s):  
Optical Sensor ◽  
High Speed ◽  
Dynamic Range ◽  
Sensor Arrays ◽  
High Dynamic Range ◽  
High Dynamic

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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