Zinuo Li

University of Strathclyde

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Yu Chen

University of Strathclyde

Arivazhagan Valluvar Oli

University of Luxembourg

Dong Xiao

Hubei Water Resources Research Institute

Chi‐Man Pun

University of Macau

Shenghong Luo

University of Macau

Aruna Ivaturi

University of Strathclyde

Weiwen Chen

University of Macau

Zhenya Zang

University of Strathclyde

Quan Wang

University of Strathclyde

Xuhang Chen

Huizhou University

Cooperative Institutions

University of Strathclyde

University of Macau

National Yang Ming Chiao Tung University

University of Electronic Science and Technology of China

Huizhou University

University of Cambridge

University of Chinese Academy of Sciences

Shenzhen Institutes of Advanced Technology

Chinese Academy of Sciences

University of Luxembourg

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UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale Transformer

arXiv (Cornell University) (2023)

Xuhang Chen Zinuo Li Shenghong Luo Weiwen Chen Shuqiang Wang

Underwater images often exhibit poor quality, imbalanced coloration, and low contrast due to the complex and intricate interaction of light, water, and objects. Despite the significant contributions of previous underwater enhancement techniques, there exist several problems that demand further improvement: (i) Current deep learning methodologies depend on Convolutional Neural Networks (CNNs) that lack multi-scale enhancement and also have limited global perception fields. (ii) The scarcity of paired real-world underwater datasets poses a considerable challenge, and the utilization of synthetic image pairs risks overfitting. To address the aforementioned issues, this paper presents a Multi-scale Transformer-based Network called UWFormer for enhancing images at multiple frequencies via semi-supervised learning, in which we propose a Nonlinear Frequency-aware Attention mechanism and a Multi-Scale Fusion Feed-forward Network for low-frequency enhancement. Additionally, we introduce a specialized underwater semi-supervised training strategy, proposing a Subaqueous Perceptual Loss function to generate reliable pseudo labels. Experiments using full-reference and non-reference underwater benchmarks demonstrate that our method outperforms state-of-the-art methods in terms of both quantity and visual quality.

Overfitting

10.48550/arxiv.2310.20210

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Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine

Sensors (2022)

Zhenya Zang Dong Xiao Quan Wang Zinuo Li Wujun Xie

We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM), using the extreme learning machine (ELM). We used extensive metrics to evaluate ELM and existing algorithms. First, we compared these algorithms using synthetic datasets. The results indicate that ELM can obtain higher fidelity, even in low-photon conditions. Afterwards, we used ELM to retrieve lifetime components from human prostate cancer cells loaded with gold nanosensors, showing that ELM also outperforms the iterative fitting and non-fitting algorithms. By comparing ELM with a computational efficient neural network, ELM achieves comparable accuracy with less training and inference time. As there is no back-propagation process for ELM during the training phase, the training speed is much higher than existing neural network approaches. The proposed strategy is promising for edge computing with online training.

Extreme Learning Machine

10.3390/s22103758

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Near-Ultraviolet Indoor Black Light-Harvesting Perovskite Solar Cells

ACS Applied Energy Materials (2022)

Arivazhagan Valluvar Oli Zinuo Li Yu Chen Aruna Ivaturi

Indoor light-energy-harvesting solar cells have long-standing history with perovskite solar cells (PSCs) recently emerging as potential candidates with high power conversion efficiencies (PCEs). However, almost all of the reported studies on indoor light-harvesting solar cells utilize white light in the visible wavelength. Low wavelength near-ultraviolet (UV) lights used under indoor environments are not given attention despite their high photon energy. In this study, perovskite solar cells have been investigated for the first time for harvesting energy from a commercially available near-UV (UV-A) indoor LED light (395-400 nm). Also called black lights, these near-UV lights are commonly used for decoration (e.g., in bars, pubs, aquariums, parties, clubs, body art studios, neon lights, and Christmas and Halloween decorations). The optimized perovskite solar cells with the

Ultraviolet

Sunlight

Ultraviolet light

10.1021/acsaem.2c01560

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Citations (14)