3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
This addresses noise reduction in hyperspectral images, which is crucial for applications like remote sensing, but it appears incremental as it builds on existing neural network approaches with specific architectural modifications.
The paper tackles hyperspectral image denoising by proposing a 3D quasi-recurrent neural network that embeds structural spatio-spectral and global spectral correlations, achieving significant improvements in restoration accuracy and computation time over state-of-the-art methods under various noise settings.
In this paper, we propose an alternating directional 3D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge -- structural spatio-spectral correlation and global correlation along spectrum. Specifically, 3D convolution is utilized to extract structural spatio-spectral correlation in an HSI, while a quasi-recurrent pooling function is employed to capture the global correlation along spectrum. Moreover, alternating directional structure is introduced to eliminate the causal dependency with no additional computation cost. The proposed model is capable of modeling spatio-spectral dependency while preserving the flexibility towards HSIs with arbitrary number of bands. Extensive experiments on HSI denoising demonstrate significant improvement over state-of-the-arts under various noise settings, in terms of both restoration accuracy and computation time. Our code is available at https://github.com/Vandermode/QRNN3D.