A Joint Total Variational Denoising Technique for Hyperspectral Imaging

Hyperspectral imaging (HSI) involves capturing a scene across a broad spectrum of electromagnetic wavelengths to facilitate detailed analysis and predictive applications. Nev-ertheless, noise introduced during the acquisition process poses a significant challenge for the imaging system. In hyperspectral imaging (HSI), this noise generally consists of a blend of Gaussian and impulse types, making efficient noise reduction, or denoising, crucial for accurate image analysis. This study proposes a maximum a posteriori (MAP) based denoising solution, for eliminating the presence of mixed Gaussian-impulse noise in HSI, by constructing a joint optimization model for both noise distributions. The proposed solution utilizes the primal-dual hybrid gradient descent (PDHG) algorithm for solving and minimizing the joint optimization model. Rigorous tests on both synthetic and real hyperspectral imaging datasets demonstrate the efficacy of the suggested noise reduction technique.