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Multi-scale integration with semantic embedding and adaptive excitation transformer for underwater optical image enhancement

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Optics and Laser Technology 2025年 189卷

作者： Yang, Jing Liang, Hui Zhu, Shanbing Bai, Shumin Jiang, Fengling Hussain, Amir School of Artificial Intelligence and Big Data Hefei University Hefei230601 China Collaborative Innovation Laboratory for Computer Vision and Pattern Recognition Hefei University Hefei230601 China Hefei Normal University Hefei230601 China School of Computing Edinburgh Napier University Merchiston Campus Scotland EdinburghEH10 5DT United Kingdom

As an important technology in the domains of ocean exploration and underwater robotics, underwater optical image enhancement has drawn significant attention. However, underwater images suffer from severe degradation due to wavelength-related optical attenuation, refraction, and scattering. The need to acquire high-quality underwater optical images poses challenges to current techniques. A dual-branch neural network based on multi-scale integration with semantic embedding and adaptive excitation transformer for underwater optical image enhancement is proposed in this paper. First, a semantic embedding network serving as the semantic branch is introduced to extract low- and high-level semantic features effectively to generate images with clearer edges and texture details. Concurrently, an enhancement branch containing adaptive excitation transformer is constructed to enhance local details while concentrating on global information. Since different feature channels correspond to different patterns of the input image, an adaptive excitation mechanism is deployed to achieve adaptive estimations of channel weights, highlighting the more representative channels while penalizing less important channels to enrich texture and eliminate blurring and color deviations. Additionally, a multi-scale dynamic integration module is designed. It establishes a correlation between the semantic and enhancement branches and adaptively selects prominent features to avoid over-enhancement and improve clarity and color deviation. Both the qualitative and quantitative results evaluated on public underwater optical image datasets show that our method outperforms the state-of-the-art methods in terms of subjective perception and evaluation metrics, indicating outstanding learning and generalization capabilities. Furthermore, excellent performance highlights the substantial benefits it contributes to downstream visual-related engineering tasks. © 2025 Elsevier Ltd

关键词： Adaptive optics

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HR_BMNet: A boundary perception and multiresolution fusion network for semantic segmentation of remote sensing

HR_BMNet: A boundary perception and multiresolution fusion n...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Chen, Yan Wang, Mengyuan Jiang, Wenxiang Kang, Menglei Wang, Xiaofeng Collaborative Innovation Centre for Computer Vision and Pattern Recognition School of Artificial Intelligence and Big Data Hefei University Hefei230601 China

ISBN: (纸本)9781510681514

The conventional approach for semantic segmentation of remote sensing imagery using encoder-decoder convolutional neural networks relies on the output of prior feature maps sequentially without considering the interactions between neighboring contextual feature maps with multiple resolutions. While the standard HRNet proposal has successfully improved multi-resolution semantic and spatial features to address the aforementioned issues, its lack of emphasis on boundary perception often results in inadequate target segmentation. Furthermore, a frequent occurrence of multiresolution contextual interaction in HRNet leads to the addition of a significant quantity of redundant information and amplifies the complexity of the model. Hence, to tackle the abovementioned issues, we propose a semantic segmentation network identified as HR-BMNet, which incorporates boundary sensitivity and multiple-resolution learning. The idea associated with standard HRNet is adopted as the foundational architecture. We extend novel boundary perception and multi-resolution fusion attention modules, integrating channel attention mechanisms. The strategy provides an ex-tensive optimization of edges and the efficient capture of crucial multi-scale features. During the feature combination stage, the boundary insights are employed to augment the semantic information, thereby mitigating the spatial details loss, enhancing the intra-class semantic consistency, and achieving superior segmentation. The efficacy of the proposed method is validated through comparison and ablation experiments conducted on the ISPRS Vaihingen and CSRSD datasets. Among the experiments conducted, the best ones attained a mean Intersection over Union (mIoU) of 72.11% on the Vaihingen dataset and 89.28% on the CSRSD dataset, respectively. © 2024 SPIE.

关键词： Semantic Segmentation

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Single-image depth estimation based on multi-scale feature fusion

Single-image depth estimation based on multi-scale feature f...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Wang, Handong He, Lixin Zhou, Chengying Yang, Jing Cheng, Zhi Cao, Shenjie Collaborative Innovation Centre for Computer Vision and Pattern Recognition School of Artificial Intelligence and Big Data Hefei University Hefei230601 China School of Electronic and Information Engineering Anhui Jianzhu University No. 292 Ziyun Road Hefei230009 China

ISBN: (纸本)9781510681514

Begin Single-image depth estimation holds significant application value in the field of computer vision. Traditional methods have certain limitations in accurately locating distant regions in images and predicting the precision of object edges. Therefore, this paper proposes a depth estimation method from a single image using fusion of multi-scale features. Firstly, we construct a feature extraction module that combines Transformer and multi-scale feature fusion mechanisms. This module effectively captures features at different scales and levels in images while focusing on global features, resulting in a more comprehensive and precise feature representation. Secondly, we design a Scene Feature Encoding Module that utilizes dilated convolution blocks and adaptive average pooling to extract context correlations between pixels in images, enabling effective recognition of distant regions. Finally, we design a Strip Spatial Perception Module to refine the perception of depth changes, thereby enhancing overall estimation accuracy. Experimental results showcase the outstanding performance of this approach across diverse scenarios, proving its practicality and wide-ranging application prospects in the field of depth estimation from a single image. © 2024 SPIE.

关键词： Image fusion

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Indic handwritten script identification using offline-online multi-modal deep network

arXiv

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arXiv 2018年

作者： Bhunia, Ayan Kumar Mukherjee, Subham Sain, Aneeshan Bhunia, Ankan Kumar Roy, Partha Pratim Pal, Umapada Institute for Media Innovation Nanyang Technological University Singapore Centre for Vision Speech and Signal Processing University of Surrey England United Kingdom Department of ECE Institute of Engineering & Management Kolkata India Department of EE Institute of Engineering & Management Kolkata India Department of Electrical Engineering Jadavpur University Department of CSE Indian Institute of Technology Roorkee India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, we propose a novel approach of word-level Indic script identification using only character-level data in training stage. Our method uses a multi-modal deep network which takes both offline and online modality of the data as input in order to explore the information from both the modalities jointly for script identification task. We take handwritten data in either modality as input and the opposite modality is generated through intermodality conversion. Thereafter, we feed this offline-online modality pair to our network. Hence, along with the advantage of utilizing information from both the modalities, the proposed framework can work for both offline and online script identification which alleviates the need for designing two separate script identification modules for individual modality. We also propose a novel conditional multi-modal fusion scheme to combine the information from offline and online modality which takes into account the original modality of the data being fed to our network and thus it combines adaptively. An exhaustive experimental study has been done on a data set including English(Roman) and 6 other official Indic scripts. Our proposed scheme outperforms traditional classifiers along with handcrafted features and deep learning based methods. Experiment results show that using only character level training data can achieve competitive performance against traditional training using word level data. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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