Fixed Pattern Noise Removal for Multi-View Single-Sensor Infrared Camera

Abstract

Fixed pattern noise (FPN) is a temporally coherent noise present on videos due to the non-uniformities in the response of the imaging sensor. It is a common problem for infrared videos which degrades the quality of the observation and hinders subsequent applications. In this work we introduce a generalization of the FPN removal problem where the input data consists of several different sequences with the same FPN. This is motivated by infrared cameras that capture multiple views with a single sensor via a periodic motion pattern of a mirror or the camera itself, such as those used in surveillance. This multi-view setting allows for a much more accurate estimation of the FPN in comparison with the standard FPN removal problem from a single view. We propose a novel energy minimization approach for multi-view FPN removal, and two optimization algorithms that can be applied both in an off-line and on-line manner. In addition, we show that the proposed energy can be adapted to the problem of FPN removal from a single view with a rolling window approach, obtaining a significant improvement over the state of the art. We demonstrate the performance of the proposed method with synthetic data and real data from surveillance infrared cameras.