Adaptive Dual-Frequency Denoising Network-Based Strip Non-Uniformity Correction Method for Uncooled Long Wave Infrared Camera

The imaging quality of uncooled long wave infrared (IR) cameras is always limited by the stripe non-uniformity mainly caused by fixed pattern noise (FPN). In this paper, we propose an adaptive dual-frequency denoising network-based stripe non-uniformity correction (NUC) method, namely ADFDNet, to realize the balance between FPN removal and image detail preservation. Our ADFDNet takes the dual-frequency feature deconstruction module as its core, which decomposes the IR image into high-frequency and low-frequency features, and performs targeted processing through detail enhancement branches and sparse denoising branches. The former enhances the performance of detail preservation through multi-scale convolution and pixel attention mechanism, while the latter combines sparse attention mechanism and dilated convolution design to suppress high-frequency FPN. Furthermore, the dynamic weight fusion of features is realized using the adaptive dual-frequency fusion module, which better integrates detail information. In our study, a 420-pair image dataset covering different noise levels is constructed for better model training and evaluation. Experiments verify that the presented ADFDNet method significantly improves image clarity in both real and simulated noise scenes, and achieves a better balance between FPN suppression and detail preservation than other existing methods.