Retinex-Based Low-Light Hyperspectral Restoration Using Camera Response Model

Spectral quality is one of the most critical issues that has to be considered in real hyperspectral image (HSI) application. Denoising, destriping, inpainting, deblurring and super-resolution are common techniques to improve the quality of HSIs from different aspects. These techniques have attracted much attention that a diversity of methods, algorithms, tools have been well developed to facilitate the development of HSI restoration. Although effectively improving the quality of HSIs, these technologies mainly focus on recovering an HSI captured in the normal sunlight. It is acknowledged that HSIs are captured via passive imaging mechanisms covering the spectral bands from visible& near-infrared to shortwave infrared spectral range (i.e., around 400nm to 2500nm). The imaging condition limits HSI spectrometers to capture HSIs without sunlight (e.g., in dark environments or night time). In this work, a low-light HSI restoration method is proposed, where we borrow idea of intrinsic decomposition based on Retinex theory in natural image low-light enhancement. Additionally, camera response function that describe the spectral degradation of RGB image and relationship between irradiance and pixel values are employed, respectively. The experimental results validate the effectiveness of the proposed method.