Diffused Convolutional Neural Network for Hyperspectral Image Super-Resolution

Abstract

With the rapid development of deep convolutional neural networks, super-resolution (SR) in hyperspectral image (HSI) has achieved good results. Current methods generally use two dimensional (2D) convolution for feature extraction, but they cannot effectively extract spectral information. Although three dimensional (3D) convolution can better characterize feature structure of HSI, it will lead to parameter redundancy, model complexity and severe memory shortage. To address above problems, we propose a new hyperspectral image super-resolution method, named diffused convolutional neural network (DCNN). Specifically, spectral convolutions have been added into the enhanced convolutional neural (ECN) block, and a series of spectral convolutions is introduced in the residual network to learn features in the channel direction of different depths. Further, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to retain the shape and texture information of the image respectively, which can well represent the spatial structure of the object. In order to effectively make use of the extracted shallow and deep features, a feature fusion strategy is employed to reinforce the reconstruction efficiency. Besides, an image enhancement module has been developed to diffuse the super-resolution image into the image space. Extensive evaluations and comparisons show that our DCNN approach can not only recover the HSI data with richer details, but also achieve superiority over several state-of-the-art methods.