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.