FSPnP: Plug-and-Play Frequency–Spatial-Domain Hybrid Denoiser for Thermal Infrared Image

Maritime thermal infrared (IR) imaging is susceptible to various types of mixed noise interference, such as Gaussian, stripe, and internal stray radiation noise, due to atmospheric radiation and complex dynamic backgrounds. These factors directly hinder subsequent image processing applications such as target detection and tracking. In this article, we propose a hybrid denoising model based on a model reconstruction framework to jointly address nonuniformity issues in single-frame thermal IR images. Stripe noise, due to its directional nature, exhibits distinct physical characteristics in both the spatial and frequency domains. Most existing methods rely on designing prior constraints based on features from only one domain. To adapt to diverse stripe distribution patterns, we introduce spatial priors for stripes in the hybrid denoising model, concurrently pioneering the introduction of frequency-domain priors in a plug-and-play technology for the first time. This approach facilitates mutual enhancement of multidomain characteristic constraints in stripe removal. Furthermore, we use polynomial fitting to characterize the distribution of internal stray radiation noise, which possesses global smoothness characteristics. The hybrid model effectively mitigates the impact of stripe distribution on the smoothing correction. Experiments demonstrate the effectiveness and superior performance of the proposed hybrid model in addressing mixed noise. Furthermore, we degraded the hybrid model into a destriping model and demonstrated its outstanding performance in the standalone destriping task. The proposed method can provide valuable references for the restoration of thermal IR or hyperspectral remote sensing images and offer additional information for IR target detection and tracking tasks.