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Grant: Canada Research Chairs

York University

$100,000.00

Computer Vision

FY 2024-25

Summary
My research focuses on the technological and algorithmic underpinnings that allow digital cameras to sense the physical world for photographic and scientific purposes. I am specifically interested in the processing steps – collectively termed the camera pipeline – applied by onboard digital cameras to convert the raw unprocessed sensor's response to scene radiance to the final output image. The current camera pipeline architecture has changed little in the last 20 years and uses a photo-centric design with the primary goal of producing visually pleasing photographs. However, the basic imaging sensor used in virtually all cameras, from DSLRs to smartphones, can capture accurate information about the physical environment suitable for scientific and medical applications. One of the principal objectives of my research initiative is to explore innovative camera pipeline designs facilitating dual-purpose functionality for both scientific and photographic applications. In pursuit of this, my emphasis lies predominantly on smartphone camera pipelines, given that smartphone platforms now represent the most prevalent and accessible means of imaging. Another integral facet of my research program involves exploring how artificial intelligence (AI) can be successfully incorporated into the "next-generation" camera pipeline design. While conventional photo-centric camera pipelines rely heavily on signal processing algorithms, AI-based methods, especially neural networks, have exhibited remarkable efficacy in routine camera pipeline tasks such as image denoising, digital zoom, and multi-frame fusion. The transition from signal-processing pipelines to a neural camera pipeline opens up new possibilities to reimagine every aspect of the camera experience, ranging from enhanced image quality for photographic and scientific applications to superior image encoding formats and compression. The overarching goal of this research program is to shape the future landscape of camera-based imaging technology, catering to a diverse array of applications.
Program
Canada Research Chairs
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
CRC-2023-00283
Name nom
Brown, Michael
Department département
Electrical Engineering and Computer Science (EECS) - York
Organization id
38
Institution établissement
York University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2023
Award amount
100000
Program id
CRC
Program name en
Canada Research Chairs
Program name fr
Chaires de recherche du Canada
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
9998
Committee name en
Canada Research Chair Selection Committee
Committee name fr
Comité de sélection des Chaires de recherche du Canada
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Non disponible
Research subject en
Not available
Research subject fr
Non disponible
Field of research list codes
RDF1020112
Field of research list names en
Computer vision in artificial intelligence
Field of research list names fr
Vision par ordinateur en intelligence artificielle
Socioeconomic objective list codes
RDS10609
Socioeconomic objective list names en
Computer, electronic, optical products and communication equipment
Socioeconomic objective list names fr
Produits informatiques, électroniques et optiques et équipement de communication
Application title
Computer Vision
Keywords
Color science; In-camera processing; Computer Vision; Image Signal Processors (ISPs); Color Processing
Application summary
My research focuses on the technological and algorithmic underpinnings that allow digital cameras to sense the physical world for photographic and scientific purposes. I am specifically interested in the processing steps – collectively termed the camera pipeline – applied by onboard digital cameras to convert the raw unprocessed sensor's response to scene radiance to the final output image. The current camera pipeline architecture has changed little in the last 20 years and uses a photo-centric design with the primary goal of producing visually pleasing photographs. However, the basic imaging sensor used in virtually all cameras, from DSLRs to smartphones, can capture accurate information about the physical environment suitable for scientific and medical applications. One of the principal objectives of my research initiative is to explore innovative camera pipeline designs facilitating dual-purpose functionality for both scientific and photographic applications. In pursuit of this, my emphasis lies predominantly on smartphone camera pipelines, given that smartphone platforms now represent the most prevalent and accessible means of imaging. Another integral facet of my research program involves exploring how artificial intelligence (AI) can be successfully incorporated into the "next-generation" camera pipeline design. While conventional photo-centric camera pipelines rely heavily on signal processing algorithms, AI-based methods, especially neural networks, have exhibited remarkable efficacy in routine camera pipeline tasks such as image denoising, digital zoom, and multi-frame fusion. The transition from signal-processing pipelines to a neural camera pipeline opens up new possibilities to reimagine every aspect of the camera experience, ranging from enhanced image quality for photographic and scientific applications to superior image encoding formats and compression. The overarching goal of this research program is to shape the future landscape of camera-based imaging technology, catering to a diverse array of applications.
York University - $100,000 Grant (2024-25) | Canada Spends