Event Location: NSH 1507
Bio: Neel Joshi is a Postdoctoral Researcher at Microsoft Research. He
recently completed his Ph.D. in Computer Science at UC San Diego where
he was advised by Dr. David Kriegman. His research interests include
computer vision and graphics, specifically computational photography and
video, data-driven graphics, and appearance measurement and modeling.
Previously, he earned his Sc.B. in Computer Science from Brown
University and his M.S. in Computer Science from Stanford University. He
has also held internships at Mitsubishi Electric Research Labs (MERL),
Adobe Systems, and Microsoft Research.

Abstract: The digital imaging revolution has made the camera practically
ubiquitous; however, image quality has not improved with increased
camera availability, and image artifacts such as blur, noise, and poor
color-balance are still quite prevalent. As a result, there is a strong
need for simple, automatic, and accurate methods for image correction.
Correcting these artifacts, however, is challenging, as problems such as
deblurring, denoising, and color-correction are ill-posed, where the
number of unknown values outweighs the number of observations. As a
result, it is necessary to add additional prior information as constraints.

In this talk, I will present three aspects of my dissertation on
performing image enhancement using content-specific image models and
priors, i.e. models tuned to a particular image. First, I will discuss
my work in methods that learn from a photographer’s image collection,
where I use identity-specific priors to perform corrections for images
containing faces. These methods introduce an intuitive paradigm for
image enhancement, where users fix images by simply providing examples
of good photos from their personal photo album. Second, I will discuss
a fast blur estimation method which uses a model that all edges in a
sharp image are step-edges. Lastly, I will discuss a framework for image
deblurring and denoising that uses local color statistics to produce
sharp, low-noise results.