Scene-based dual domain non-uniformity correction algorithm for stripe and optics-caused fixed pattern noise removal

Abstract

Non-uniformity is a long-standing problem that significantly degrades infrared images through fixed pattern noise (FPN). Existing scene-based algorithms for non-uniformity correction (NUC) effectively eliminate stripe FPN assuming consistent inter-frame non-uniformity. However, they are ineffective in handling spatially continuous optical FPN. In this paper, a scene-based dual domain correction approach is proposed to address the non-uniformity problem by simultaneously removing stripe and optics-caused FPN. We achieve this through gain correction in the frequency domain and offset correction in the spatial domain. To remove stripes, we approximate the desired image using a guided filter and iteratively update the bias correction parameters frame by frame. For optics-caused noise removal, we separate low frequency noise from the scene using Fourier transform and update the gain correction parameters accordingly. To mitigate ghost artifacts, a combined strategy is introduced to adaptively adjusts learning rates and weights during the updating stage. Comprehensive evaluations demonstrate that our proposed approach outperforms compared methods in both real and simulated non-uniformity infrared videos.