Naga Nithin Katta | Image Processing | Best Researcher Award

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Mr. Naga Nithin Katta | Image Processing | Best Researcher Award

Employee at Oppo | India

Naga Nithin Katta is a highly motivated computer science and engineering professional with a strong focus on innovation, research, and problem-solving. His expertise spans artificial intelligence, machine learning, computer vision, and full stack development, areas in which he has applied his skills to impactful projects. He has gained industrial exposure as a software engineer at OPPO, where he contributed to projects involving video stream analysis, automation of testing frameworks, and mobile AI deployment. Alongside his industry experience, he has been an active mentor in data structures and algorithms, helping students strengthen their problem-solving abilities. His leadership has been recognized through international competitions, including selection among the Top 100 teams globally in the Google Solution Challenge and multiple hackathon victories. With a balance of technical knowledge, practical implementation, and a passion for community contribution, he is steadily building a strong foundation as an emerging researcher with promising leadership potential.

Professional Profile

Scopus Profile

Education

Naga Nithin Katta is pursuing a Bachelor of Technology in Computer Science and Engineering at VNR Vignana Jyothi Institute of Engineering and Technology, where he has been developing a strong academic background in computing principles, software engineering, and applied technologies. Prior to this, he successfully completed a diploma in computer science from the Government Institute of Electronics, which provided him with a solid technical base in programming, database management, and system design. His educational journey has been complemented by active participation in research-oriented projects, hackathons, and collaborative learning platforms that encouraged innovation and problem-solving. He has consistently demonstrated academic excellence by integrating classroom knowledge with practical applications, which is evident in his project work and international recognition through competitive platforms. This strong educational foundation has equipped him with both theoretical and applied perspectives, allowing him to bridge the gap between academia and industry while nurturing his passion for research and development.

Professional Experience

Naga Nithin Katta has gained valuable professional experience as a software engineer at OPPO, where he contributed to significant projects aimed at improving efficiency and automation in mobile technologies. His work involved developing web applications using Vue.js and MySQL for managing project statuses, implementing video stream analysis through OpenCV and Python, and deploying AI models on mobile devices using ONNX and Beeware. He played a key role in creating a UI automation system powered by large language models, reducing manual testing efforts and enhancing accuracy. Additionally, he contributed to building a network operator testing automation tool, streamlining processes and reducing workforce requirements. Alongside his industry work, he served as a student mentor at SmartInterviews, guiding learners in data structures and algorithms and preparing them for technical challenges. This blend of industrial expertise and teaching experience reflects his versatility, ability to collaborate across teams, and passion for applying research in practical contexts.

Research Interest

Naga Nithin Katta’s research interests lie primarily in the fields of artificial intelligence, computer vision, natural language processing, and software engineering, with a particular focus on developing innovative solutions that bridge academic research and real-world applications. He has worked on projects such as sign language converters that integrate computer vision with generative AI and cloud technologies, reflecting his interest in human-computer interaction and accessibility-focused applications. His engagement with large language models and UI automation tools demonstrates his curiosity in advancing human-machine interaction and automated testing frameworks. Additionally, his focus on video stream analysis and frame detection highlights his inclination towards multimedia research and visual computing. He is also keen on exploring areas such as deep learning optimization, mobile AI deployment, and cloud-integrated intelligent systems. His vision is to contribute to impactful solutions that enhance everyday technologies while simultaneously pursuing scholarly outputs that advance scientific knowledge.

Research Skill

Naga Nithin Katta has developed strong research skills that enable him to design, implement, and evaluate innovative solutions across different domains of computer science. He is proficient in programming languages such as C, C++, Python, and Java, and demonstrates advanced knowledge in full stack development with tools like ReactJs, Vue.js, and MySQL. His expertise in AI and machine learning is reflected in projects involving computer vision, natural language processing, and model deployment on mobile devices. He has practical experience in research-driven software development, having implemented algorithms for video frame detection, gesture recognition, and UI automation powered by large language models. His familiarity with tools like OpenCV, ONNX, Flask, and cloud-based APIs allows him to conduct applied research efficiently. He also possesses strong problem-solving abilities, demonstrated by his role as a mentor in data structures and algorithms. His skills in bridging theoretical concepts with industrial applications showcase his potential as a future research leader.

Publications Top Notes

Title: Optical Motion Detection Language Generator: A Survey

Year: 2025

Conclusion

Naga Nithin Katta is a deserving candidate for the Best Researcher Award as he has consistently demonstrated innovation, technical expertise, and leadership in both academic and industrial settings. His impactful projects, including advancements in computer vision, automation, and AI-driven solutions, showcase contributions that address real-world challenges and benefit society. With proven recognition in global competitions, mentorship roles, and industry research experience, he has already made meaningful strides as an emerging researcher. With a continued focus on publishing in reputed venues and building stronger international collaborations, he holds significant potential to become a future leader in the research and technology community.

Yuanyuan QIN | Medical Image Analysis | Best Researcher Award

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Dr . Yuanyuan QIN | Medical Image Analysis | Best Researcher Award

Associate Chief Physician, Associate Professor at  Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , China

Dr. Yuanyuan Qin is a distinguished Associate Chief Physician and Associate Professor in the Department of Radiology at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. With a strong foundation in clinical radiology and advanced neuroimaging, she has dedicated her career to investigating brain disorders such as Alzheimer’s disease, Parkinson’s disease, and post-COVID neurological changes. Her research integrates multimodal imaging techniques with cognitive neuroscience and machine learning, emphasizing non-invasive diagnostic innovations. A recipient of national research funding and provincial awards, Dr. Qin has published extensively in leading international journals, with several high-impact and highly cited articles. She has also demonstrated leadership in academic teaching through the adoption of blended learning methodologies. Her interdisciplinary expertise, clinical insights, and research productivity make her a recognized contributor to the fields of radiology and neuroscience, with ongoing work focused on understanding neural mechanisms underlying cognitive decline and aging-related diseases.

Professional Profile 

Education🎓

Dr. Yuanyuan Qin pursued her advanced medical education at Huazhong University of Science and Technology, completing a prestigious Combined Master-PhD program between 2008 and 2013. Her doctoral training emphasized advanced neuroimaging techniques, with research exploring structural and functional brain alterations in neurodegenerative conditions. During her PhD, she was selected for a one-year joint PhD training program at the esteemed Johns Hopkins University in the United States (2011–2012), where she gained international exposure to state-of-the-art imaging methodologies and collaborative research environments. Her interdisciplinary education integrated clinical radiology, neuroscience, and data-driven analysis, laying a strong foundation for her later research on cognitive disorders and aging. This cross-institutional and cross-national academic background not only enriched her scientific expertise but also cultivated her capacity to approach radiological challenges from both a clinical and research perspective. Her academic training continues to inform her innovative work in diagnostic imaging and cognitive neurodegeneration.

Professional Experience📝

Dr. Yuanyuan Qin has built a progressive and impactful professional career in radiology at Tongji Hospital. Starting in 2013 as a Resident Physician, she rapidly advanced through roles as an Attending Physician (2014–2019), Associate Chief Physician (2019–2020), and ultimately to Associate Professor (2020–present). Throughout her career, she has been deeply engaged in clinical diagnostics, medical imaging interpretation, and mentoring medical students and interns. Her dual roles in academic and clinical settings have allowed her to integrate patient-centered care with research-led innovation. Dr. Qin’s experience spans routine radiological evaluations to complex imaging studies in patients with neurological and neurodegenerative conditions. She has actively contributed to improving radiology internship training programs through digital platforms and 3D simulation tools. Her leadership within the department is recognized not only in her clinical acumen but also in fostering collaborative research projects and guiding junior physicians and researchers in translational imaging studies.

Research Interest🔎

Dr. Yuanyuan Qin’s research interests lie at the intersection of neuroimaging, cognitive neuroscience, and clinical radiology. Her primary focus is on understanding the neural mechanisms of neurodegenerative diseases, particularly Alzheimer’s and Parkinson’s, through advanced magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and functional connectivity analysis. She is also deeply interested in the application of repetitive transcranial magnetic stimulation (rTMS) combined with cognitive training as a therapeutic and diagnostic tool. Recently, her research has extended into the neurological impacts of COVID-19, exploring long-term cerebral microstructure changes in asymptomatic patients. Her work frequently integrates artificial intelligence, deep learning, and image quantification methods to improve diagnostic precision. Dr. Qin is committed to developing non-invasive imaging biomarkers that can track disease progression and predict cognitive decline. Her interdisciplinary approach bridges clinical needs with technological advancement, contributing valuable insights into early detection and intervention strategies for aging-related cognitive disorders.

Award and Honor🏆

Dr. Yuanyuan Qin has been widely recognized for her academic excellence and scientific contributions. Notably, she received the First Prize in the 2019 Hubei Provincial Science and Technology Progress Award for her innovative work on the integration of structural and functional MRI in brain development and aging-related diseases. In teaching, she earned the Second Prize in the 2020 Young Teacher Teaching Competition at Huazhong University of Science and Technology, highlighting her commitment to educational innovation. As a principal investigator, she has secured multiple competitive grants, including a General Program and a Youth Project from the National Natural Science Foundation of China (NSFC). Her work has garnered national attention and peer acknowledgment, with multiple publications cited widely in top-tier journals. Her research article in the Journal of Clinical Investigation is listed as a Highly Cited Article, further validating her impact in the fields of radiology and neuroscience.

Research Skill🔬

Dr. Yuanyuan Qin possesses a robust portfolio of research skills, especially in neuroimaging analysis, multimodal MRI, and diffusion tensor imaging (DTI). She has extensive expertise in image processing platforms such as 3D-Slicer, FSL, and SPM, along with experience in deep learning algorithms for radiological quantification. Her technical strengths extend to designing and conducting longitudinal studies, particularly in evaluating cognitive interventions like rTMS paired with cognitive training in Alzheimer’s patients. She demonstrates proficiency in integrating clinical data with imaging outcomes to derive meaningful correlations for disease diagnosis and prognosis. Additionally, she has contributed to the development of automated MRI quantification pipelines, including those for Parkinsonism index assessment. Her interdisciplinary methods often incorporate statistical modeling, functional connectivity analysis, and AI-based imaging biomarker discovery. These research capabilities position her as a key contributor in translating complex neuroimaging insights into real-world clinical applications.

Conclusion💡

Dr. Yuanyuan Qin is highly suitable for the Best Researcher Award based on her exceptional track record in neuroimaging research, consistent national-level funding, scientific leadership in Alzheimer’s and Parkinson’s research, and significant contributions to radiological education and practice. Her trajectory exemplifies a balance between academic rigor, innovation, and clinical relevance.

Publications Top Noted✍

  1. Title: Surface-Based Vertexwise Analysis of Morphometry and Microstructural Integrity for White Matter Tracts in Diffusion Tensor Imaging: With Application to the Corpus Callosum in Alzheimer’s Disease
    Authors: Tang, Xiaoying; Qin, Yuanyuan; Zhu, Wenzhen; Miller, Michael I.
    Year: 2017
    Citation: Human Brain Mapping, DOI: 10.1002/hbm.23491

  2. Title: Atlas-based deep gray matter and white matter analysis in Alzheimer’s disease: diffusion abnormality and correlation with cognitive function
    Authors: Qin Yuanyuan; Zhang Shun; Guo Linying; Zhang Min; Zhu Wenzhen
    Year: 2016
    Citation: Chinese Journal of Radiology, WOSUID: CSCD:5699935

  3. Title: Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer’s disease
    Authors: Tang, Xiaoying; Qin, Yuanyuan; Wu, Jiong; Zhang, Min; Zhu, Wenzhen; Miller, Michael I.
    Year: 2016
    Citation: Magnetic Resonance Imaging, DOI: 10.1016/j.mri.2016.05.001

  4. Title: Simulating the Evolution of Functional Brain Networks in Alzheimer’s Disease: Exploring Disease Dynamics from the Perspective of Global Activity
    Authors: Li, Wei; Wang, Miao; Zhu, Wenzhen; Qin, Yuanyuan; Huang, Yue; Chen, Xi
    Year: 2016
    Citation: Scientific Reports, DOI: 10.1038/srep34156

  5. Title: Frequency-specific Alterations of Large-scale Functional Brain Networks in Patients with Alzheimer’s Disease
    Authors: Qin, Yuan-Yuan; Li, Ya-Peng; Zhang, Shun; Xiong, Ying; Guo, Lin-Ying; Yang, Shi-Qi; Yao, Yi-Hao; Li, Wei; Zhu, Wen-Zhen
    Year: 2015
    Citation: Chinese Medical Journal, DOI: 10.4103/0366-6999.151654

  6. Title: Frequency-specific Alterations of Large-scale Functional Brain Networks in Patients with Alzheimer’s Disease (Correction)
    Authors: Qin, Y. Y.; Li, Y. P.; Zhang, S.; Xiong, Y.; Guo, L. Y.; Yang, S. Q.
    Year: 2015
    Citation: Chinese Medical Journal, DOI: 10.4103/0366-6999.156150

  7. Title: An Efficient Approach for Differentiating Alzheimer’s Disease from Normal Elderly Based on Multicenter MRI Using Gray-Level Invariant Features
    Authors: Li, Muwei; Oishi, Kenichi; He, Xiaohai; Qin, Yuanyuan; Gao, Fei; Mori, Susumu
    Year: 2014
    Citation: PLOS ONE, DOI: 10.1371/journal.pone.0105563

  8. Title: Discriminative Analysis of Multivariate Features from Structural MRI and Diffusion Tensor Images
    Authors: Li, Muwei; Qin, Yuanyuan; Gao, Fei; Zhu, Wenzhen; He, Xiaohai
    Year: 2014
    Citation: Magnetic Resonance Imaging, DOI: 10.1016/j.mri.2014.05.008

  9. Title: Exploring the Functional Brain Network of Alzheimer’s Disease: Based on the Computational Experiment
    Authors: Li, YaPeng; Qin, Yuanyuan; Chen, Xi; Li, Wei
    Year: 2013
    Citation: PLOS ONE, DOI: 10.1371/journal.pone.0073186

  10. Title: Gross Feature Recognition of Anatomical Images Based on Atlas Grid (GAIA)
    Authors: Qin, Yuan-Yuan; Hsu, Johnny T.; Yoshida, Shoko; Faria, Andreia V.; Oishi, Kumiko; et al.
    Year: 2013
    Citation: NeuroImage: Clinical, DOI: 10.1016/j.nicl.2013.08.006

  11. Title: In vivo Quantitative Whole-brain Diffusion Tensor Imaging Analysis of APP/PS1 Transgenic Mice
    Authors: Qin, Yuan-Yuan; Li, Mu-Wei; Zhang, Shun; Zhang, Yan; Zhao, Ling-Yun; et al.
    Year: 2013
    Citation: Neuroradiology, DOI: 10.1007/s00234-013-1195-0

  12. Title: The Functional Brain Network Changes of Alzheimer’s Disease
    Authors: Li YaPeng; Qin YuanYuan; Li Wei
    Year: 2013
    Citation: Chinese Journal of Medical Physics, WOSUID: CSCD:5004621

  13. Title: Voxel-Based Diffusion Tensor Imaging of an APP/PS1 Mouse Model of Alzheimer’s Disease
    Authors: Shu, Xiaogang; Qin, Yuan-Yuan; Zhang, Shun; Jiang, Jing-Jing; Zhang, Yan; et al.
    Year: 2013
    Citation: Molecular Neurobiology, DOI: 10.1007/s12035-013-8418-6

  14. Title: Stromal Cell-Derived Factor 1 Alpha Decreases Beta-Amyloid Deposition in Alzheimer’s Disease Mouse Model
    Authors: Wang, Qi; Xu, Yi; Chen, Jin-Cao; Qin, Yuan-Yuan; Liu, Mao; et al.
    Year: 2012
    Citation: Brain Research, DOI: 10.1016/j.brainres.2012.04.011

 

Dibyalekha Nayak | Computer vision | Women Researcher Award

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Dr . Dibyalekha Nayak | Computer vision | Women Researcher Award

Assistant professor at Shah and Anchor Kutchhi Engineering College, India

Dr. Dibyalekha Nayak is a dedicated academician and emerging researcher with deep expertise in image processing, adaptive compression, and VLSI design. Her professional journey is marked by a strong commitment to teaching, scholarly research, and technological advancement. With over a decade of teaching experience and a recently completed Ph.D. from KIIT University, Bhubaneswar, her research has produced several publications in SCI-indexed journals and international conferences. Dr. Nayak’s contributions reflect an interdisciplinary approach, combining deep learning techniques with low-power hardware design to address complex challenges in wireless sensor networks and multimedia systems. She has actively participated in faculty development programs and technical workshops, continuously upgrading her knowledge. Her professional philosophy emphasizes ethics, hard work, and continuous learning. Currently serving as an Assistant Professor at Shah and Anchor Kutchi Engineering College in Mumbai, she aspires to make impactful contributions to the field of electronics and communication through research, innovation, and collaboration.

Professional Profile 

Education🎓

Dr. Dibyalekha Nayak holds a Ph.D. in Image Processing from the School of Electronics at KIIT University, Bhubaneswar, where she completed her research between September 2018 and May 2024. Her doctoral work focused on advanced techniques in image compression and saliency detection using deep learning and compressive sensing. She completed her Master of Technology (M.Tech) in VLSI Design from Satyabhama University, Chennai, in 2011, graduating with a commendable CGPA of 8.33. Prior to that, she earned her Bachelor of Engineering (B.E.) in Electronics and Telecommunication from Biju Patnaik University of Technology (BPUT), Odisha, in 2008, with a CGPA of 6.5. Her academic background provides a strong foundation in both theoretical electronics and practical applications in image processing and circuit design. The combination of image processing and VLSI design throughout her academic journey has enabled her to engage in cross-disciplinary research and foster innovation in both hardware and software domains.

Professional Experience📝

Dr. Dibyalekha Nayak has accumulated over 12 years of rich academic experience in various reputed engineering institutions across India. Currently, she serves as an Assistant Professor at Shah and Anchor Kutchi Engineering College, Mumbai, affiliated with Mumbai University, where she joined in July 2024. Prior to this, she worked as a Research Scholar at KIIT University (2018–2024), contributing significantly to image processing research. Her earlier roles include Assistant Professor positions at institutions such as College of Engineering Bhubaneswar (2016–2018), SIES Graduate School of Technology, Mumbai (2014), St. Francis Institute of Technology, Mumbai (2013), and Madha Engineering College, Chennai (2011–2012). Across these roles, she has taught a variety of undergraduate and postgraduate courses, supervised student projects, and contributed to departmental development. Her teaching areas span digital electronics, VLSI design, image processing, and communication systems, demonstrating a strong alignment between her teaching and research activities.

Research Interest🔎

Dr. Dibyalekha Nayak’s research interests lie at the intersection of image processing, deep learning, and VLSI design, with a special focus on adaptive compression, saliency detection, and compressive sensing. Her doctoral research addressed the development of innovative, low-complexity algorithms for image compression using techniques like block truncation coding and DCT, tailored for wireless sensor network applications. She is also deeply interested in integrating deep learning frameworks into image enhancement and compression tasks to improve performance in real-world environments. Additionally, her background in VLSI design supports her interest in low-power hardware architectures for efficient implementation of image processing algorithms. Dr. Nayak is particularly motivated by research problems that bridge the gap between theoretical innovation and practical implementation, especially in the fields of embedded systems and multimedia communication. Her interdisciplinary research aims to create scalable, energy-efficient, and intelligent solutions for future communication and sensing technologies.

Award and Honor🏆

While Dr. Dibyalekha Nayak’s profile does not explicitly mention formal awards or honors, her scholarly achievements speak volumes about her academic excellence and dedication. She has published multiple research articles in prestigious SCI and Web of Science indexed journals such as Multimedia Tools and Applications, Mathematics, and Computers, reflecting the quality and impact of her research. She has been actively involved in reputed international conferences including IEEE and Springer Lecture Notes, where she has presented and published her research findings. Her work on saliency-based image compression and fuzzy rule-based adaptive block compressive sensing has received commendation for its innovation and applicability. Furthermore, her selection and sustained work as a Research Scholar at KIIT University for over five years highlights the recognition she has earned within academic circles. Her consistent participation in technical workshops, faculty development programs, and collaborations also demonstrate her growing reputation and standing in the field of electronics and image processing.

Research Skill🔬

Dr. Dibyalekha Nayak possesses a versatile and robust set of research skills aligned with modern-day challenges in image processing and electronics. She is proficient in developing image compression algorithms, saliency detection models, and adaptive techniques using block truncation coding, fuzzy logic, and DCT-based quantization. Her technical expertise extends to deep learning architectures tailored for image enhancement and compressive sensing in wireless sensor networks. Additionally, she has a strong command of VLSI design methodologies, enabling her to work on low-power circuit design and hardware implementation strategies. Dr. Nayak is also skilled in scientific programming, using tools such as MATLAB and Python, along with LaTeX for research documentation. She has a clear understanding of research methodologies, simulation frameworks, and performance analysis metrics. Her experience in preparing manuscripts for SCI-indexed journals and conference presentations showcases her technical writing abilities. Overall, her analytical mindset and hands-on skills make her a competent and impactful researcher.

Conclusion💡

Dr. Dibyalekha Nayak is a highly dedicated and emerging researcher in the fields of Image Processing, Deep Learning, and VLSI. Her academic journey reflects perseverance, scholarly depth, and a clear focus on impactful research. Her SCI-indexed publications, teaching experience, and cross-domain knowledge make her a deserving candidate for the Best Researcher Award.

Publications Top Noted✍

  • Title: Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application
    Authors: D. Nayak, K. Ray, T. Kar, S.N. Mohanty
    Journal: Mathematics, Volume 11, Issue 7, Article 1660
    Year: 2023
    Citations: 6

  • Title: A novel saliency based image compression algorithm using low complexity block truncation coding
    Authors: D. Nayak, K.B. Ray, T. Kar, C. Kwan
    Journal: Multimedia Tools and Applications, Volume 82, Issue 30, Pages 47367–47385
    Year: 2023
    Citations: 4

  • Title: Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization
    Authors: D. Nayak, K. Ray, T. Kar, C. Kwan
    Journal: Computers, Volume 11, Issue 7, Article 110
    Year: 2022
    Citations: 3

  • Title: Sparsity based Adaptive BCS color image compression for IoT and WSN Application
    Authors: D. Nayak, T. Kar, K. Ray
    Journal: Signal, Image and Video Processing, Volume 19, Issue 8, Pages 1–7
    Year: 2025

  • Title: Hybrid Image Compression Using DCT and Autoencoder
    Authors: D. Nayak, T. Kar, K. Ray, J.V.R. Ravindra, S.N. Mohanty
    Conference: 2024 IEEE Pune Section International Conference (PuneCon), Pages 1–6
    Year: 2024

  • Title: Performance Comparison of Different CS based Reconstruction Methods for WSN Application
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: 2021 IEEE 2nd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC)
    Year: 2021

  • Title: A Comparative Analysis of BTC Variants
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: Proceedings of International Conference on Communication, Circuits, and Systems (LNEE, Springer)
    Year: 2021

  • Title: Low Power Error Detector Design by using Low Power Flip Flops Logic
    Authors: D. Chaini, P. Malgi, S. Lopes
    Journal: International Journal of Computer Applications, ISSN 0975-8887
    Year: 2014

Dr. Abdelaziz Daoudi | Image Processing | Best Researcher Award

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Dr. Abdelaziz Daoudi | Image Processing | Best Researcher Award

Doctorate at Tahri Mohammed university, Algeria

👨‍🎓 Profiles

Scopus

Google Scholar

Publications

Enhancing Brain Segmentation in MRI through Integration of Hidden Markov Random Field Model and Whale Optimization Algorithm

  • Authors: Abdelaziz Daoudi, Saïd Mahmoudi
    Journal: Computers
    Year: 2024

Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets

  • Authors: Catalina Tobon-Gomez, Arjan J Geers, Jochen Peters, Jürgen Weese, Karen Pinto, Rashed Karim, Mohammed Ammar, Abdelaziz Daoudi, Jan Margeta, Zulma Sandoval, Birgit Stender, Yefeng Zheng, Maria A Zuluaga, Julian Betancur, Nicholas Ayache, Mohammed Amine Chikh, Jean-Louis Dillenseger, B Michael Kelm, Saïd Mahmoudi, Sébastien Ourselin, Alexander Schlaefer, Tobias Schaeffter, Reza Razavi, Kawal S Rhode
    Journal: IEEE transactions on medical imaging
    Year: 2015

Prof Dr. Oliver Steinbock | Image Processing and Enhancement | Best Researcher Award

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Prof Dr. Oliver Steinbock | Image Processing and Enhancement | Best Researcher Award

Florida State University, United States

👨‍🎓 Profiles

Scopus

Orcid

Publications

Understanding the Salt Crystallizations from Droplets under Various Gravity and Pressure Environments: Display of the Marangoni Effect?

  • Authors: Hadidi, R.; Pinckney, V.D.; Shaw, S.A.; Steinbock, O.; Dangi, B.B.
    Journal: Journal of Physical Chemistry B
    Year: 2025

High-throughput robotic collection, imaging, and machine learning analysis of salt patterns: composition and concentration from dried droplet photos

  • Authors: Batista, B.C.; Amrutha, S.V.; Yan, J.; Dangi, B.B.; Steinbock, O.
    Journal: Digital Discovery
    Year: 2025

Wavebreakers in excitable systems and possible applications for corrosion mitigation

  • Authors: Batista, B.C.; Romanovskaia, E.V.; Romanovski, V.I.; Kiss, I.Z.; Steinbock, O.
    Journal: Chaos
    Year: 2025

Morphogenic Modeling of Corrosion Reveals Complex Effects of Intermetallic Particles

  • Authors: Batista, B.C.; Romanovskaia, E.V.; Romanovski, V.I.; Scully, J.R.; Steinbock, O.
    Journal: Advanced Science
    Year: 2024

Chemical composition from photos: Dried solution drops reveal a morphogenetic tree

  • Authors: Batista, B.C.; Tekle, S.D.; Yan, J.; Dangi, B.B.; Steinbock, O.
    Journal: Proceedings of the National Academy of Sciences of the United States of America (PNAS)
    Year: 2024

Assoc Prof Dr. Peixian Zhuang | Image Processing | Best Researcher Award

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Assoc Prof Dr. Peixian Zhuang | Image Processing | Best Researcher Award

Peixian Zhuang at University of Science and Technology Beijing, China

👨‍🎓 Profiles

Scopus

Google Scholar

Publications

CVANet: Cascaded visual attention network for single image super-resolution

  • Authors: Weidong Zhang, Wenyi Zhao, Jia Li, Peixian Zhuang, Haihan Sun, Yibo Xu, Chongyi Li
  • Journal: Neural Networks
  • Year: 2024

Underwater image enhancement via piecewise color correction and dual prior optimized contrast enhancement

  • Authors: Weidong Zhang, Songlin Jin, Peixian Zhuang, Zheng Liang, Chongyi Li
  • Journal: IEEE Signal Processing Letters
  • Year: 2023

Non-uniform illumination underwater image restoration via illumination channel sparsity prior

  • Authors: Guojia Hou, Nan Li, Peixian Zhuang, Kunqian Li, Haihan Sun, Chongyi Li
  • Journal: IEEE Transactions on Circuits and Systems for Video Technology
  • Year: 2023

Gacnet: Generate adversarial-driven cross-aware network for hyperspectral wheat variety identification

  • Authors: Weidong Zhang, Zexu Li, Guohou Li, Peixian Zhuang, Guojia Hou, Qiang Zhang, Chongyi Li
  • Journal: IEEE Transactions on Geoscience and Remote Sensing
  • Year: 2023

Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement

  • Authors: Weidong Zhang, Peixian Zhuang, Hai-Han Sun, Guohou Li, Sam Kwong, Chongyi Li
  • Journal: IEEE Transactions on Image Processing
  • Year: 2022

Dr. Yue Cao | Image Processing | Best Researcher Award

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Dr. Yue Cao | Image Processing | Best Researcher Award

Doctorate at Harbin Institute of Technology, China

Profile

Google Scholar

📋 Summary

Yue Cao is a Ph.D. candidate at Harbin Institute of Technology, specializing in computational imaging. His work focuses on noise modeling and image enhancement in extreme low-light conditions, making significant strides in sensor technology and image processing.

Education

  • Ph.D., Computer Application Technology (Sep. 2020 – Present), Harbin Institute of Technology
  • M.S., Computer Software and Theory (Sep. 2017 – Aug. 2020), Shaanxi Normal University
  • B.S., Software Engineering (Sep. 2010 – Aug. 2014), Inner Mongolia University

💼 Internship Experience

  • Industry-Academia-Research Project Intern
    OPPO Research Institute, Shenzhen
    Feb. 2023 – Feb. 2024 (estimated)
    Engaged in noise modeling and parameter calibration of mobile phone sensors, as well as joint denoising, demosaicking, and super-resolution tasks in extremely low-light conditions related to smartphone sensor technology.

🏆 Selected Awards

  • 2020: Winner Award, NTIRE Real Image Denoising Challenge – rawRGB Track
  • 2020: 3rd Place Award, NTIRE Real Image Denoising Challenge – sRGB Track
  • 2014: Outstanding Graduate of Inner Mongolia Autonomous Region, P.R. China
  • 2012: Excellent Three Merit Student Award of Inner Mongolia Autonomous Region, P.R. China

🔬 Research Interests

Dr. Yue’s research interests include noise modeling, image denoising, demosaicking, super-resolution, and sensor technology, with a particular focus on improving imaging performance in challenging environments.

Publications

Physics-guided iso-dependent sensor noise modeling for extreme low-light photography

  • Authors: Yue Cao, Ming Liu, Shuai Liu, Xiaotao Wang, Lei Lei, Wangmeng Zuo
  • Year: 2023

Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment

  • Authors: Yue Cao, Zhaolin Wan, Dongwei Ren, Zifei Yan, Wangmeng Zuo
  • Year: 2022

Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser

  • Authors: Yue Cao, Xiaohe Wu, Shuran Qi, Xiao Liu, Zhongqin Wu, Wangmeng Zuo
  • Year: 2021

Unpaired learning of deep image denoising

  • Authors: Xiaohe Wu, Ming Liu, Yue Cao, Dongwei Ren, Wangmeng Zuo
  • Year: 2020

Ntire 2020 challenge on real image denoising: Dataset, methods and results

  • Authors: Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte, Michael S Brown
  • Year: 2020

Prof. Zhi Gao | Image Processing | Best Researcher Award

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Prof. Zhi Gao, Image Processing, Best Researcher Award

Zhi Gao at Wuhan University, China

Professional Profile

Summary:

Zhi Gao is a highly accomplished Professor and Doctoral Supervisor at the School of Remote Sensing and Information Engineering, Wuhan University. He holds prestigious positions as the National Young Talent Program and Distinguished Professor of Hubei Province, China. With a solid background in engineering and extensive experience in academia and research, he has built strong collaborative networks with renowned universities and institutions worldwide.

👩‍🎓Education:

Zhi Gao received his Bachelor of Engineering (B.Eng.) and Doctor of Philosophy (Ph.D.) degrees from Wuhan University, China, in 2002 and 2007, respectively. His educational background provides him with a strong foundation in his field.

🧬 Work Experience:

Zhi Gao’s professional journey reflects a wealth of experience in both academia and industry. Highlights of his career include:

  • Research Fellow (A) and Project Manager at the Interactive and Digital Media Institute, National University of Singapore (NUS), Singapore, since 2008.
  • Research Scientist (A) at the Temasek Laboratories, NUS, contributing significantly to research endeavors.
  • Building strong collaborative relationships with prestigious institutions globally, including the Temasek Laboratory, National University of Singapore, Carnegie Mellon University Robotics Institute, Robert Gordon University, The Chinese University of Hong Kong, Beijing Normal University, and Beijing Institute of Technology.

Research Interests:

  • Computer Vision
  • Machine Learning
  • Remote Sensing
  • UAV-based Surveillance Research and Applications

Publications Top Noted:

Paper Title: Exploring the relationship between land use change patterns and variation in environmental factors within urban agglomeration
  • Authors: Xiao, R., Yin, H., Liu, R., Liu, L., Jia, T.
  • Journal: Sustainable Cities and Society
  • Volume: 108
  • Pages: 105447
  • Year: 2024
  • Citations: 0
Paper Title: Tracking by Detection: Robust Indoor RGB-D Odometry Leveraging Key Local Manhattan World
  • Authors: Zhou, Z., Gao, Z., Xu, J.
  • Journal: IEEE Robotics and Automation Letters
  • Volume: 9
  • Issue: 6
  • Pages: 4990–4997
  • Year: 2024
Paper Title: How Challenging is a Challenge? CEMS: a Challenge Evaluation Module for SLAM Visual Perception
  • Authors: Zhao, X., Gao, Z., Li, H., Fang, H., Chen, B.M.
  • Journal: Journal of Intelligent and Robotic Systems: Theory and Applications
  • Volume: 110
  • Issue: 1
  • Pages: 42
  • Year: 2024
  • Citations: 0
Paper Title: TJ-FlyingFish: An Unmanned Morphable Aerial–Aquatic Vehicle System
  • Authors: Liu, X., Dou, M., Yan, R., Chen, J., Chen, B.M.
  • Journal: Unmanned Systems
  • Volume: 12
  • Issue: 2
  • Pages: 409–428
  • Year: 2024
  • Citations: 1
Paper Title: WaterFormer: A Global-Local Transformer for Underwater Image Enhancement With Environment Adaptor
  • Authors: Wen, J., Cui, J., Yang, G., Dou, L., Chen, B.M.
  • Journal: IEEE Robotics and Automation Magazine
  • Volume: 31
  • Issue: 1
  • Pages: 29–40
  • Year: 2024
  • Citations: 1