D2Net: Deep Denoising Network in Frequency Domain for Hyperspectral Image
Erting Pan, Yong Ma, Xiaoguang Mei, Jun Huang, Fan Fan, Jiayi Ma
Abstract
Dear Editor, Since the existing hyperspectral image denoising methods suffer from excessive or incomplete denoising, leading to information distortion and loss, this letter proposes a deep denoising network in the frequency domain, termed D2Net. Our motivation stems from the observation that images from different hyperspectral image (HSI) bands share the same structural and contextual features while the reflectance variations in the spectra are mainly fallen on the details and textures. We design the D2Net in three steps: 1) spatial decomposition, 2) spatial-spectral denoising, and 3) refined reconstruction. It achieves multi-scale feature learning without information loss by adopting the rigorous symmetric discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT). In particular, the specific design for different frequency components ensures complete noise removal and preservation of fine details. Experiment results demonstrate that our D2Net can attain a promising denoising performance.