NUC algorithm by calculating the corresponding statistics of the decomposed signal Parul Goyal

Infrared focal plane arrays (IRFPA) imaging systems are strongly affected by the spatial non-uniformity of the IRFPA response. The non-uniformity is due to the pixel-to-pixel responsivity (gain) and dark current (offset) variations of the IRFPA. The non-uniformity results in a fixed spatial noise superimposed on the infrared image, which can completely mask the useful thermal signatures in an infrared scene. What makes this problem even more challenging is that spatial non-uniformity drifts temporally as a result of variations in the IRFPA working conditions. Such drift require a real-time compensation for IRFPA's response in the course of imaging system operation]. Numerous non-uniformity correction (NUC) techniques have been developed. Traditional Reference-based calibration techniques rely on the known blackbody calibration-source. But they can't suppress drift of IRFPA's response effectively. Now non-uniformity correction techniques are being focused on some scene-based algorithms. Essentially, scene-based techniques identify the true infrared image from the fixed-pattern noise by exploiting motion-related features in the image. I. Introduction In numerical analysis and functional analysis, a discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information.