Kernel Regression Image Processing Method for Optical Readout MEMS based Uncooled IRFPA

摘要

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.