Cooled infrared detector arrays always suffer from undesired Ripple Fixed-Pattern Noise (FPN) when observe the scene of sky. The Ripple Fixed-Pattern Noise seriously affect the imaging quality of thermal imager, especially for small target detection and tracking. It is hard to eliminate the FPN by the Calibration based techniques and the current scene-based nonuniformity algorithms. In this paper, we present a modified space low-pass and temporal high-pass nonuniformity correction algorithm using adaptive time domain threshold (THP&GM). The threshold is designed to significantly reduce ghosting artifacts. We test the algorithm on real infrared in comparison to several previously published methods. This algorithm not only can effectively correct common FPN such as Stripe, but also has obviously advantage compared with the current methods in terms of detail protection and convergence speed, especially for Ripple FPN correction. Furthermore, we display our architecture with a prototype built on a Xilinx Virtex-5 XC5VLX50T field-programmable gate array (FPGA). The hardware implementation of the algorithm based on FPGA has two advantages: (1) low resources consumption, and (2) small hardware delay (less than 20 lines). The hardware has been successfully applied in actual system.
In this paper, we propose a novel scene-based non-uniformity correction algorithm for infrared image processing-temporal high-pass non-uniformity correction algorithm based on grayscale mapping (THP and GM). The main sources of non-uniformity are: (1) detector fabrication inaccuracies; (2) non-linearity and variations in the read-out electronics and (3) optical path effects. The non-uniformity will be reduced by non-uniformity correction (NUC) algorithms. The NUC algorithms are often divided into calibration-based non-uniformity correction (CBNUC) algorithms and scene-based non-uniformity correction (SBNUC) algorithms. As non-uniformity drifts temporally, CBNUC algorithms must be repeated by inserting a uniform radiation source which SBNUC algorithms do not need into the view, so the SBNUC algorithm becomes an essential part of infrared imaging system. The SBNUC algorithms’ poor robustness often leads two defects: artifacts and over-correction, meanwhile due to complicated calculation process and large storage consumption, hardware implementation of the SBNUC algorithms is difficult, especially in Field Programmable Gate Array (FPGA) platform. The THP and GM algorithm proposed in this paper can eliminate the non-uniformity without causing defects. The hardware implementation of the algorithm only based on FPGA has two advantages: (1) low resources consumption, and (2) small hardware delay: less than 20 lines, it can be transplanted to a variety of infrared detectors equipped with FPGA image processing module, it can reduce the stripe non-uniformity and the ripple non-uniformity.
The leakage of toxic or hazardous gases not only pollutes the environment, but also threatens people's lives and property safety. Many countries attach great importance to the rapid and effective gas leak detection technology and instrument development. However, the gas leak imaging detection systems currently existing are generally limited to a narrow-band in Medium Wavelength Infrared (MWIR) or Long Wavelength Infrared (LWIR) cooled focal plane imaging, which is difficult to detect the common kinds of the leaking gases. Besides the costly cooled focal plane array is utilized, the application promotion is severely limited. To address this issue, a wide-band gas leak IR imaging detection system using Uncooled Focal Plane Array (UFPA) detector is proposed, which is composed of wide-band IR optical lens, sub-band filters and switching device, wide-band UFPA detector, video processing and system control circuit. A wide-band (3µm~12µm) UFPA detector is obtained by replacing the protection window and optimizing the structural parameters of the detector. A large relative aperture (F#=0.75) wide-band (3μm~12μm) multispectral IR lens is developed by using the focus compensation method, which combining the thickness of the narrow-band filters. The gas leak IR image quality and the detection sensitivity are improved by using the IR image Non-Uniformity Correction (NUC) technology and Digital Detail Enhancement (DDE) technology. The wide-band gas leak IR imaging detection system using UFPA detector takes full advantage of the wide-band (MWIR&LWIR) response characteristic of the UFPA detector and the digital image processing technology to provide the resulting gas leak video easy to be observed for the human eyes. Many kinds of gases, which are not visible to the naked eyes, can be sensitively detected and visualized. The designed system has many commendable advantages, such as scanning a wide range simultaneously, locating the leaking source quickly, visualizing the gas plume intuitively and so on. The simulation experiment shows that the gas IR imaging detection has great advantages and widely promotion space compared with the traditional techniques, such as point-contact or line-contactless detection.
The quality of infrared imaging system was limited by the non-uniformity (NU) in the Infrared Focal Plane
Array(IRFPA), especially in the uncooled infrared imaging system. Scene based non-uniformity correction (SBNUC)
algorithms are widely concerned since they only need the readout infrared data captured by the imaging system during its
normal operation. However, there still exists the problem of ghost artifact in the algorithms, and their performance is
noticeably degraded when the methods are applied over scenes with lack of motion. In addition, most SBNUC
algorithms are difficult to be implemented in the hardware.
In this paper, to reduce the fringe NU in uncooled VOx IRFPA we present a simple and effective SBNUC method based
on Constant Statistics in which the fringe NU is reduced by balancing the statistics of the vertical channels. Through
analyzing the reason of ghost artifact being brought in in the SBNUC algorithms, our algorithm successfully reduce the
ghost artifact that plagues SBNUC algorithms through the use of optimization techniques in the parameter
estimation .The advantage of the algorithm lies in its simplicity and low computational complexity. Our algorithm is
implemented on a FPGA hardware platform with XC5VSX50T as the kernel processor, the raw infrared data are
provided by an uncooled infrared focal plane array of VOx which has fringe NU. Our processing system reaches high
correction levels, fringe NU being reduced, the ghost artifact being decreased, which can lay a technical foundation for
the following study and applications of high performance thermal imaging system.