Thermal Hyperspectral Image Denoising Using Total Variation Based on Bidirectional Estimation and Brightness Temperature Smoothing

Compared with visible and near-infrared images, the long-wave infrared region hyperspectral image (LWIR HSI) is more vulnerable to noise pollution in the acquisition process due to its specific imaging mode. In this letter, a new restoration method is proposed using total variation based on bidirectional estimation and brightness temperature smoothing (BBSTV), which can remove dead lines and restore junk bands effectively. The proposed method introduces the linear relation between brightness temperature and emissivity derived from radiative transfer model (RTM) to restoration processing. Besides, bilateral estimation is used to complete the loss information of noise-polluted bands to achieve a faster convergence speed of total variation (TV) method. Both simulated and real LWIR HSI experiments were conducted to verify the improvements of the BBSTV method in quantitative and qualitative ways.