LMS-based enhancement for IR images with applications to surveillance

Abstract

In this paper, a new strategy is presented to map the pixel space from infrared (IR) image to the pixel space of optical images. This is done by utilizing the usual technique of system identification using the input-output measurements and trying to fit a parametric model under a given fitness criterion. In this case, the IR image is taken as the input data and a similar image taken simultaneously by a normal CCD camera is taken as the output data. The acquired data are then fitted with an autoregressive moving average (ARMA) model in 1D under the H ∞ criterion which is the most robust and statistically less-dependent fitness criterion. The well known LMS algorithm can reach the H ∞ bounds under certain impositions. By structuring the given data to suite these impositions, the LMS algorithm is used to reach the optimal bound. This implies a faster convergence with better performance. The results have shown remarkable enhancement in the images and appears to be promising for the real-time applications.