Rizwan Khan
Thesis Title: '' ill-light image and multiview high dynamic range image enhancement ''
Through years of dedicated research and academic training, I have built a solid foundation in computer vision and image processing, particularly in areas like artificial intelligence, advanced machine learning and deep learning methods for real-world visual challenges and practical applications. As a computer science researcher specializing in image processing and computer vision, I am dedicated to transforming our approach to complex challenges in high-level and low-level vision. I hold a Master's degree in Electrical Engineering and completed my Ph.D. in Information and Communications Engineering from the School of Electronic Information and Communication Engineering (EIC) Huazhong University of Science and Technology (HUST), Wuhan, PRC (September 2018-2021). My doctoral research focused on Image Processing and Computer Vision leveraging Machine Learning and deep learning for the color and contrast enhancement of low-light images, utilizing both single-view and multiview camera systems.
During my Ph.D., I was actively involved in research at the Wuhan National Lab of Optoelectronics (WNLO, HUST, PRC). My work there in the Lab encompassed image acquisition, multiview camera systems, high-speed imaging, LIDARs, light-field cameras, and the GigE Vision interface for controlling multiview setups. I tackled challenges across multiple lighting scenarios to advance image relighting, enhancement, and high dynamic range imaging. My efforts included capturing large-scale datasets and developing various algorithms and learning-based techniques (using TensorFlow) to address low-level vision problems.
My extensive research has equipped me with a deep understanding of image processing, computer vision, and deep learning. I have honed my skills in scientific research, mathematical modeling, and simulations, particularly in the context of image processing for single-view and multiview camera images. With a proven track record of developing innovative solutions for image-related challenges, I am committed to pushing the boundaries of what is possible in this field.
My passion for developing solutions to complex image-processing problems, especially in low-light and underwater conditions, has driven me to specialize in these areas. I have made significant contributions through my research, including the development of algorithms that integrate game-theoretic optimization and deep learning techniques. My work has been published in renowned journals such as Pattern Recognition, Expert Systems with Applications, Optics Express, Neurosciences, and various IEEE journals.
I have a robust experience of working with computer vision, image processing and food technologies. I am adept in developing modern concept for practical and state of the art applications. I am at ease with working on the modern experimental devices. I have diverse educational experience and knowledge in engineering and computer science and have many unique skills in simulating engineering and software designs that make me a good team player in any technical team.
Proficient in programming languages like Python, I have been engineering software-based solutions for image processing and computer vision-based applications for over five years. I am eager to collaborate with like-minded individuals and organizations in the field of image processing and computer vision. Together, we can develop groundbreaking solutions that unlock new levels of performance and create real-world impacts in visual data applications. In addition to this technical proficiency I also have obtained advance scientific research based mathematical modelling and simulation skills through which I can transform the data into practical applications with my hands on knowledge and practical experience.
I am up-to-date with the advancement and modern technologies in my research domain and my continuous research publications provide a strong evidence on it.
In my research on image processing and computer vision, I prioritize three core values: innovation, collaboration, and commitment. Innovation is fundamental, enabling me to tackle new challenges in areas such as low-light image enhancement and intelligent detection, developing solutions that push the boundaries of what is possible in computer vision. Collaboration is essential, as it fosters knowledge sharing and opens opportunities for interdisciplinary learning, where I exchange insights and methodologies with colleagues, students, and industry experts. Lastly, commitment is at the heart of my academic and professional development, driving my dedication to continually refine my skills, contribute meaningfully to my field, and mentor students and emerging researchers.
Purpose and meaning of my work: The basic purpose is to remove the gap between the theoretical knowledge for the practical implementation in the form of real world applications using artificial intelligence and machine learning. Meaning of my work is to design the novel technologies for the identification and detection of the features, which are not visible through naked eye. Thus I am focused to desing machine learning based automated systems which are suitable for the accurate detection and have wide range of real world and medical imaging applications.
Reflection on my future career choices: I am aiming at a faculty position in the domain of computer science and engineering. I have several years of practical industrial and research experience. My career so far after PhD aligns with the drive for gathering new knowledge for teaching and to guide students in scientific research. The research experience in academic and industrial setting for commercial and educational projects with published work in each domain is building a foundation for project management, teaching and mentorship role, where I can apply my expertise and values to inspire students and contribute to the field. I believer Teaching offers a balance between research and sharing technical skills, aligning with my goals to impact both the academic and industrial sectors in computer vision.
My values:
Innovation, to solve new problems
Collaboration, for knowledge sharing
Commitment, for academic and professional development