Xiaoxiao Zhou

The development status of the micro-cantilever array based un-cooled IR detectors

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.

A crossing path scene-based algorithm for nonuniformity correction in focal-plane array sensors

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.

Super-resolution of wavefront encoding system by combining bi-cubic interpolation and L-R filtering methods

In the wavefront encoding optical system, a low-resolution sensor is adopted to capture encoded image. Through bi-cubic interpolation and L-R filtering using the PSF as the deconvolution filter, the super-resolution image is achieved.

Holographic compensation-based optical readout technique for microcantilever IR image system

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.

Scene-based nonuniformity correction algorithm for MEMS-based un-cooled IR image system

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.

Study on the particle-filter-based motion filtering algorithm for digital image stabilization systems

Towards to the existent problems in current motion filtering algorithms such as the low-pass filter, the Kalman filter and the extended Kalman filter. We proposed a novel motion filtering algorithm base on particle filter to separate random jitter from the global motion vectors. In this method, the variation of global motion parameters are regarded as the state-variable of system, the uniform motion model of camera is used, and the motion filtering is carried out to dynamic image sequences according to the features of digital image stabilization systems. Experimental results prove that this particle-filter-based motion filtering algorithm can achieve real-time filtering effect, and the filtering effect can be affected by the number of the particles only and almost irrespectively with other factors. This method can be used agilely and is very suitable for digital image stabilization systems application. We realized the programs based on the TI TMS320C6416DSP processing chip and got very perfect experiment results.

Kernel regression image processing method for optical readout MEMS based uncooled IRFPA

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 fixed pattern noise (FPN) is complicated by the fact that the response of each FPA detector changes due to a variety of factors. We adapt and expand kernel regression ideas for use in image denoising. 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.

A sparse signal representation-based image denoising algorithm for uncooled MEMS IRFPA

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.

Effective Method to Improve the Lens F # of Un-cooled IR Detector Systems

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.

The effective image denoising method for MEMS based IR image arrays

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.