Chao Zhou

Office 708, Malet Place Engineering Building,

Torrington Place,

London, WC1E 7JE United Kingdom

Hi! I'm Chao (Eddy) Zhou, I obtain my PhD under the supervision of Prof.Miguel Rodrigues, in Information, Inference and Machine Learning (I2|ML)group at UCL, London, UK, in 2023. I obtained my Bachelor degree in Communication Engineering at WHUT in 2015 and Master Degree in Information and Communication Systems at UESTC in 2018, respectively.

My recent research interests include Science4AI, representation learning and generative machine learning.

news

May, 2024	I am joining the Relational Machine Learning Group at CISPA as a postdoc under the supervision of Dr. Rebekka Burkholz in May 2024.
October, 2023	Our work 'BITS-Net: Blind Image Transparency Separation Network' is presented at ICIP 2023, Kuala Lumpur, Malaysia.
August, 2023	Our work 'Hyperspectral Blind Unmixing using a Double Deep Image Prior' with Prof. Miguel has been accepted at TNNLS 2023.
30 Jun, 2023	I passed my PhD viva with minor corrections on 30 Jun 2023! Thanks to my supervisor Prof. Miguel Rodrigues for arranging this viva. Thanks to internal examiner Prof. Ioannis (Yiannis) Andreopoulos from UCL and external examiner Dr. Wei Dai from Imperial College London.

selected publications

Journal

Hyperspectral Blind Unmixing Using a Double Deep Image Prior

C. Zhou, and M. R. D. Rodrigues

in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2023.3294714.

Abs PDF Code

With the rise of machine learning, hyperspectral image (HSI) unmixing problems have been tackled using learning-based methods. However, physically meaningful unmixing results are not guaranteed without proper guidance. In this work, we propose an unsupervised framework inspired by deep image prior (DIP) that can be used for both linear and nonlinear blind unmixing models. The framework consists of three modules: 1) an Endmember estimation module using DIP (EDIP); 2) an Abundance estimation module using DIP (ADIP); and 3) a mixing module (MM). EDIP and ADIP modules generate endmembers and abundances, respectively, while MM produces a reconstruction of the HSI observations based on the postulated unmixing model. We introduce a composite loss function that applies to both linear and nonlinear unmixing models to generate meaningful unmixing results. In addition, we propose an adaptive loss weight strategy for better unmixing results in nonlinear mixing scenarios. The proposed methods outperform state-of-the-art unmixing algorithms in extensive experiments conducted on both synthetic and real datasets.