79 citations · 94 across the 4 of their papers we have counts for
4 papers
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising
Min Zhao, Jie Chen, Nicolas Dobigeon
Spectral unmixing has been extensively studied with a variety of methods and used in many applications. Recently, data-driven techniques with deep learning methods have obtained gr…
Nonlinear Hyperspectral Unmixing based on Multilinear Mixing Model using Convolutional Autoencoders
Tingting Fang, Fei Zhu, Jie Chen
Unsupervised spectral unmixing consists of representing each observed pixel as a combination of several pure materials called endmembers with their corresponding abundance fraction…
Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors
Xiuheng Wang, Jie Chen, Cédric Richard
Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices. This issue is usually add…
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing
Jie Chen, Min Zhao, Xiuheng Wang +2
Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing. Conventional physics-based models are characterized by clear interpreta…