Nonlocal Self-Similarity-Based Hyperspectral Remote Sensing Image Denoising With 3-D Convolutional Neural Network
Zhicheng Wang, Michael K. Ng, Lina Zhuang, Lianru Gao, Bing Zhang
Abstract
Recently, deep learning-based denoising methods for hyperspectral images (HSIs) have been comprehensively studied and achieved impressive performance because they can effectively extract complex and nonlinear image features. Compared with deep learning-based methods, the nonlocal similarity-based denoising methods are more suitable for images containing edges or regular textures. We propose a powerful HSI denoising method, termed NL-3DCNN, combining traditional machine learning and deep learning techniques. NL-3DCNN exploits the high spectral correlation of an HSI by using subspace representation and corresponding representation coefficients are termed eigenimages. The high spatial correlation in eigenimages is exploited by grouping nonlocal similar patches, which are denoised by a 3D convolutional neural network. The numerical and graphical denoising results of simulated and real data show that the proposed method is superior to state-of-the-art methods.