Perceptually-motivated adversarial training for deep ensemble denoising of hyperspectral images

In this letter, we present a deep-learning-based methodology for recovering hyperspectral images (HSIs) distorted by Gaussian and impulsive noise. This work makes the following contribution: To begin with, the Wasserstein Generative Adversarial Network (WGAN) is used to mitigate the effects of vanishing gradient and mode collapse that can occur when training a vanilla GAN. Secondly, data are passed via three distinct pathways in a parallel ensemble to promote multiscale feature extraction. Normal and multiscale dilated 3D convolutions are utilized to train the model in each pair of parallel paths. Thirdly, features are recovered following data permutation across three different spatial planes (viz. xy,yz, and xz planes) and after passing through parallel convolutional blocks; to promote spatio-spectral similarity within and across the different layers of the HSI data. Fourthly, by adopting Structural Similarity (SSIM) as the content loss, the issue of loss in resolution encountered during adversarial training is mitigated. Finally, the incorporation of 3D depth-wise separable convolution and batch re-normalization (BRN) solves the major issue of computational burden encountered while processing HSI data. Extensive experimental evaluation on synthetically corrupted data and real HSI data (obtained from real hyperspectral sensors) under various degradation conditions suggests that the aforementioned denoising approach could be used in real time.