Bilateral filter-based adaptive nonuniformity correction for infrared focal-plane array systems

Abstract

In scene-based nonuniformity correction (NUC) methods for infrared focal-plane array cameras, the problem of ghosting artifacts widely affects the sensitivity of the imaging system and visibly decreases the image quality. Ghosting artifacts can also degrade the performance of several applications, such as target detection and tracking. We carried out a detailed analysis of the problem using a well-established NUC technique: the least mean square Scribner's algorithm. In order to solve some drawbacks of the original Scribner's algorithm, we introduced in the scheme a new technique that mitigates ghosting. Such technique relies on the employment of an edge-preserving spatial filter for the purpose of computing reliable spatial estimates. We tested the effectiveness of the new technique applying the improved NUC method to an experimental IR sequence of frames acquired in the laboratory. Finally, the performance of the proposed method was discussed and compared to that yielded by a well-established deghosting technique.