Multistage Nonuniformity Correction Pipeline for Single-Frame Infrared Images Based on Hybrid High-Order Directional and Low-Rank Prior Information

High-quality infrared images are widely used in various vision tasks. However, infrared focal plane array (IRFPA) suffer from material and process limitations, resulting in non-uniform pixel response and severe spatial fixed pattern noise (FPN), which results in degraded images. In order to address the problem that existing infrared image nonuniformity correction (NUC) methods only focus on stripe noise removal but ignore the environmental noise, we propose a new multi-stage single-frame NUC processing pipeline. Firstly, for the low-rank property of infrared degraded images and the higher-order directional priori information of stripe noise, we construct an objective function which combines a higher-order gradient total variation model with a diagonal kernel paradigm as a constraint term. Multiple subproblems are solved by the alternating direction method of multipliers (ADMM) to obtain the recovered image after removal of stripe. Then, we incorporate the environmental noise into the pipeline by low-rank matrix approximationand employ singular-valued patch decomposition to efficiently separate the clean image from the noise. Extensive experiments are conducted on existing methods on real and simulated datasets, and the potential performance of the proposed method is verified in qualitative and quantitative evaluations. The code and datasets can be obtain at https://github.com/ImageVisioner/InfraredNUC.