A CMOS Integrable Oscillator-Based Front End for High-Dynamic-Range Resistive Sensors

2008 ◽  
Vol 57 (8) ◽  
pp. 1596-1604 ◽  
Author(s):  
A. De Marcellis ◽  
A. Depari ◽  
G. Ferri ◽  
A. Flammini ◽  
D. Marioli ◽  
...  
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Front End ◽  
High Dynamic

A High-Dynamic-Range Front End for an Upconversion Car-Radio Receiver

1985 ◽  
Vol 20 (3) ◽  
pp. 688-696 ◽  
Author(s):  
E.H. Nordholt ◽  
H.C. Nauta ◽  
C.A.M. Boon
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Radio Receiver ◽  
Front End ◽  
High Dynamic

UNCALIBRATED HIGH-DYNAMIC RANGE RESISTIVE SENSOR FRONT-END WITH PARALLEL CAPACITANCE ESTIMATION

2008 ◽  
Author(s):  
ANDREA DE MARCELLIS ◽  
GIUSEPPE FERRI ◽  
VINCENZO STORNELLI ◽  
ALESSANDRO DEPARI ◽  
ALESSANDRA FLAMMINI
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Front End ◽  
Resistive Sensor ◽  
High Dynamic

scholarly journals Reduced-Dimensional Capture of High-Dynamic Range Images with Compressive Sensing

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shundao Xie ◽  
Wenfang Wu ◽  
Rongjun Chen ◽  
Hong-Zhou Tan
Keyword(s):  
Compressive Sensing ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Image Sequence ◽  
Range Images ◽  
Multiple Exposure ◽  
Imaging Technology ◽  
Front End ◽  
High Dynamic ◽  
Hdr Image

The range of light illumination in real scenes is very large, and ordinary cameras can only record a small part of this range, which is far lower than the range of human eyes’ perception of light. High-dynamic range (HDR) imaging technology that has appeared in recent years can record a wider range of illumination than the perceptual range of the human eye. However, the current mainstream HDR imaging technology is to capture multiple low-dynamic range (LDR) images of the same scene with different exposures and then merge them into one HDR image, which greatly increases the amount of data captured. The advent of single-pixel cameras (compressive imaging system) has proved the feasibility of obtaining and restoring image data based on compressive sensing. Therefore, this paper proposes a method for reduced-dimensional capture of high dynamic range images with compressive sensing, which includes algorithms for front end (capturing) and back end (processing). At the front end, the K-SVD dictionary is used to compressive sensing the input multiple-exposure image sequence, thereby reducing the amount of data transmitted to the back end. At the back end, the Orthogonal Matching Pursuit (OMP) algorithm is used to reconstruct the input multiple-exposure image sequence. A low-rank PatchMatch algorithm is proposed to merge the reconstructed image sequence to obtain an HDR image. Simulation results show that, under the premise of reducing the complexity of the front-end equipment and the amount of communication data between the front end and the back end, the overall system achieves a good balance between the amount of calculation and the quality of the HDR image obtained.

Sign in / Sign up

Export Citation Format

Share Document