The progress of MEMS-based uncooled infrared focal plane arrays (IRFPAs) are one of the most successful
examples of integrated MEMS devices. We report on the fabrication and performance of a MEMS IRFPA based on
bimaterial microcantilever. The IR images of objects obtained by these FPAs are readout by an optical method.
However, it is difficult to avoid unwanted shape distortions in fabrication, which can degrade image quality in
many ways. In this paper, the actual manufacturing errors of FPA are widely and deeply analyzed. There are
basically two kinds of manufacturing error. The limitations of both kind of error are given. It is alse pointed out
that the detecting sensitivity has its special complexity if the shape of the FPA is not ideal flat. To overcome the
difficulties in readout process caused by manufacturing errors, a novel holographic compensating illumination
technology was given. The possibilities of actualizing this technology are analyzed in many aspects. And a model
of computer generated holographic compensation is given as a further development to be actualized in future The
experiment shows that it is a feasible way to improve system performance, especially when it is too difficult to
perfect the techniques of an FPA fabrication.
Almost two years after the investors in Sarcon Microsystems pulled the plug, the micro-cantilever array based uncooled
IR detector technology is again attracting more and more attention because of its low cost and high
credibility. An uncooled thermal detector array with low NETD is designed and fabricated using MEMS
bimaterial microcantilever structures that bend in response to thermal change. The IR images of objects obtained
by these FPAs are readout by an optical method. For the IR images, one of the most problems of FPN is
complicated by the fact that the response of each FPA detector changes due to a variety of factors, causing the
nonuniformity pattern to slowly drift in time. Thus, it is required to remove the nonuniformity. A scene-based
nonuniformity correction algorithm was discussed in this paper, against to the traditional calibration-based and
other scene-based techniques, which has the better correct performance; better MSE compared with traditional
methods can be obtained. Great compute and analysis have been realized by using the discussed algorithm to the
simulated data and real infrared scene data respectively. The experimental results demonstrate, the corrected image
by this algorithm not only yields highest Peak Signal-to-Noise Ratio values (PSNR), but also achieves best visual
quality.
MEMS have become viable systems to utilize for uncooled infrared imaging in recent years. They offer
advantages due to their simplicity, low cost and scalability to high-resolution FPAs without prohibitive increase in
cost. An uncooled thermal detector array with low NETD is designed and fabricated using MEMS bimaterial
microcantilever structures that bend in response to thermal change. The IR images of objects obtained by these
FPAs are readout by an optical method. For the IR images, processed by a sparse representation-based image
denoising and inpainting algorithm, which generalizing the K-Means clustering process, for adapting dictionaries
in order to achieve sparse signal representations. The processed image quality is improved obviously. Great
compute and analysis have been realized by using the discussed algorithm to the simulated data and in
applications on real data. The experimental results demonstrate, better RMSE and highest Peak Signal-to-Noise
Ratio (PSNR) compared with traditional methods can be obtained. At last we discuss the factors that determine the
ultimate performance of the FPA. And we indicated that one of the unique advantages of the present approach is
the scalability to larger imaging arrays.
An uncooled thermal detector array with low NETD is designed and fabricated using MEMS bimaterial
microcantilever structures that bend in response to thermal change. The IR images of objects obtained by these
FPAs are readout by an optical method. For the IR images, processed by a sparse representation-based image
denoising and inpainting algorithm, which generalizing the K-Means clustering process, for adapting dictionaries
in order to achieve sparse signal representations. The processed image quality is improved obviously. Great
compute and analysis have been realized by using the discussed algorithm to the simulated data and in
applications on real data. The experimental results demonstrate, better RMSE and highest Peak Signal-to-Noise
Ratio (PSNR) compared with traditional methods can be obtained. At last we discuss the factors that determine the
ultimate performance of the FPA. And we indicated that one of the unique advantages of the present approach is
the scalability to larger imaging arrays.
The Infrared thermal imaging systems has developments advance rapidly during the development of the research and the
manufacture technical. And its applied field has going deep into the astronautics, industry, agriculture, medical, traffic
and other fields from the national defense and military appliance. Especially in the application of the military, it has
come into being a specialty IR System Engineering field. But in many important applications, the lens calibre of the IR
thermal imaging systems often be made very large to advance the SNR of the systems. This increased the weight and the
research cost of the whole system very much. Many research indicated that the main factor to affect the image quality of
the IR systems is the fixed pattern noise (FPN) or spatial non-uniformity under the actual technical and manufacture
level. If we using the effective dynamic self-adaptive non-uniformity correction algorithms for the IR system, and use
the image enhancement technology simultaneity. We can advance the imaging quality greatly. With this plan, the
correction image we got with large F number can receive the level that uncorrected image with 1 or 2 smaller F number.
It means the lens calibre of the system will be reduced effectively. And the weight, the cubage and the research cost of
the system will be reduced greatly. It will have most important value in the applied of the actual engineering.
Almost two years after the investors in Sarcon Microsystems pulled the plug, the micro-cantilever array based
un-cooled IR detector technology is again attracting more and more attention because of its low cost and high
credibility. Recently a sort of IR imaging system consisting of micro-cantilever array and optical-readout device is
presented. The basic approach is the same: Coat the micro-cantilevers with a bi-material. The absorption of
infrared radiation causes a rise in temperature at each pixel which causes the bi-material to bend the cantilever.
The resulting change in capacitance is measured by a readout IC. The main advantage of the micro-cantilever
approach is that the temperature responsivity (as measured by the percent change in signal per degree) is
approximately ten times as large as for VOx micro-bolometers (i.e. 20-50&percent;/°C compared to 2-4&percent;/°C). In this
paper, we will discuss the following questions detailed: The imaging principle of the system, the optical-readout
principle of the imaging system, the design and produce progress of the FPA and some influence factors and
performance parameters of the system. Finally, the trends of this kind of devices will follow.
The Curvelet transform was developed from the wavelet transform. The applications of Curvelet transform reveal
its great potential in image processing due to its unique characteristics. In this paper, the theory and
implementation of Curvelet transform is summarized. The traditional Curvelet transform involves a complicated
index structure which makes the mathematics and quantitative analysis especially delicate, and it uses overlapping
windows increasing the redundancy. The Fast Curvelet Transform was discussed in this paper, which has the
optimal sparse representation. By utilizing Curvelet wrapping algorithm based on translation invariance to the
nonuniformity correction of the IRFPA, better MSE compared with traditional methods can be obtained. Great
compute and analysis have been realized by using the discussed algorithm to the simulated data and real infrared
scene data respectively. The experimental results demonstrate, the corrected image by this fast Curvelet transform
algorithm not only yields highest Peak Signal-to-Noise Ratio values (PSNR = 33.803), but also achieves best
visual quality.
During the development of the research and the manufacture technical, the Infrared thermal imaging systems has
developments advance rapidly. And its applied field has going deep into the space technology, industry, agriculture,
medical, traffic and other fields from the national defense and military appliance. Especially in the application of
the military, it has come into being a specialty IR System Engineering field. But in many important applications,
the lens calibre of the IR thermal imaging systems often be made very large to advance the SNR of the systems.
This increased the weight and the research cost of the whole system very much. Many research indicated that the
main factor to affect the image quality of the IR systems is the fixed pattern noise (FPN) or spatial non-uniformity
under the actual technical and manufacture level. If we using the effective dynamic self-adaptive non-uniformity
correction algorithms for the IR system, and use the image enhancement technology simultaneity. We can advance
the imaging quality greatly. With this plan, the correction image we got with large F number can receive the level
that uncorrected image with 1 or 2 smaller F number. It means the lens calibre of the system will be reduced
effectively. And the weight, the cubage and the research cost of the system will be reduced greatly. It will have
most important value in the applied of the actual engineering.
A recently developed scene-based nonuniformity correction algorithm for focal plane array (FPA) sensors named Crossing
Path Scene-Based Algorithm (CPSBA) is present. The goal of this thesis is to design and evaluate scene-based nonuniformity
correction algorithms that are able to suppress fixed pattern noise without need for external hardware such as temperature
reference equipment. In particular, algorithms should be able to accurately estimate motion between images and use this
knowledge to improve performance. The algorithms have been tested by using real image data from existing infrared
imaging systems with good results.
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