  Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to
improve the performance of the subsequent HSI interpretation and applications.
In this paper, a novel deep learning-based method for this task is proposed, by
learning a non-linear end-to-end mapping between the noisy and clean HSIs with
a combined spatial-spectral deep convolutional neural network (HSID-CNN). Both
the spatial and spectral information are simultaneously assigned to the
proposed network. In addition, multi-scale feature extraction and multi-level
feature representation are respectively employed to capture both the
multi-scale spatial-spectral feature and fuse the feature representations with
different levels for the final restoration. The simulated and real-data
experiments demonstrate that the proposed HSID-CNN outperforms many of the
mainstream methods in both the quantitative evaluation indexes, visual effects,
and HSI classification accuracy.
