CMU talk: Enhancing Photographs using Content-Specific Image Priors

VASC Seminar
December 15, 2008

Enhancing Photographs using Content-Specific Image Priors
Neel Joshi
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.

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.