Calibration of Imaging Sensor and Fiber Optic Taper-Caused Distortion in an X-Ray Intensified CMOS Camera

Abstract

An integrated calibration method of the imaging sensor and the fiber optic taper-caused distortion in an intensified Complementary Metal-Oxide-Semiconductor (CMOS) camera for the X-ray capture application is proposed. Generally, four kinds of distortions need to be corrected in this system: the column noise, the inconsistent pixel intensity response of whole frame, the nonlinear intensity response of each pixel, and the geometry distortion of whole frame. To conquer these problems, first, the column noise is estimated and subtracted from the captured image. The multiple measurement data processing method and the strip noise estimation technique are used. Second, a kind of two-step correction technique of the pixel intensity response is proposed to solve middle two distortion problems. The first step builds the ideal intensity response line for all pixels in image; and the second step estimates and compensates the correction error for each pixel by a polynomial function. Third a polynomial fitting method is also utilized to correct the geometry distortion. The RANdom SAmple Consensus (RANSC) computation mechanism is used to improve the calibration precision further. The optic indices of fiber optic taper and camera are considered to assist the selection of key parameters of these calibration method above. Many experiment results have proved the correctness and effectiveness of proposed method.INDEX TERMS Distortion correction, X-ray image sensor, CMOS, fiber optic taper, RANSAC.

Development of a Face Recognition System and Its Intelligent Lighting Compensation Method for Dark-Field Application

“…Second, strong imaging noise always degrades recognition accuracy. The noise exists in the imaging system extensively, such as fixed pattern noise or random noises [40]. When environment luminance is high, the signal response of the imaging sensor is strong, and the noise is submerged by imaging signal; on the contrary, the noise can be detected clearly.…”

Interference Stripe Noise Reduction of CMOS Sensor-Based Digital Holographic Measurement System

“…Because of the working principle and the manufacturing process problem, more noises will be observed from the CMOS sensor including the ISNs discussed in this paper and other speckle noises, etc. Clearly, the effective noise reduction [31] should be performed to get the high and the correct imaging details for the following computations. After a careful analysis and simulation of the optic path of the CMOS sensor-based DHM system, we think the ISNs mainly come from the complex interference of the input beams and their reflection ones which are mainly created from the unparallel assembly of the optic filter and the CMOS sensor.…”

Reflection Coefficient Estimation of Femtosecond Laser Surface Processing Using Support Vector Regression

“…Compared with the visible light sensor, the X-ray imaging camera is a kind of noise-sensitive and contrast-low sensor which uses more complex optic elements such as the fiber optic taper or image intensifier [4] to collect the weak X-ray responses from scintillator. To improve its signal response ability, a series of processes and structure design methods have been developed [5][6]. Recently, the Materials Surface Blackening Process (MSBP) [7] has become one of most important steps for high sensitivity X-ray imaging camera manufacture.…”