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1. D2Net: Deep Denoising Network in
Frequency Domain for Hyperspectral
Image
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 motiva
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
spatial decomposition, 2) spatial
reconstruction. It achieves multi
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
D2Net: Deep Denoising Network in
Frequency Domain for Hyperspectral
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
te wavelet transform (IDWT). In particular, the specific design
for different frequency components ensures complete noise removal and
D2Net: Deep Denoising Network in
Frequency Domain for Hyperspectral
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,
tion 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
in three steps: 1)
spectral denoising, and 3) refined
scale feature learning without information loss
by adopting the rigorous symmetric discrete wavelet transform (DWT) and
te wavelet transform (IDWT). In particular, the specific design
for different frequency components ensures complete noise removal and

2. preservation of fine details. Experiment results demonstrate that our D2Net
can attain a promising denoising performance.