Candidate: Dilip Krishnan
Advisors: Rob Fergus


Prof. Rob Fergus(NYU CS Advisor, Reader)
Dr. Richard Szeliski ( Microsoft research, Reader)
Prof. Yair Weiss (The Hebrew Univ. CS/Engineering, Reader)
Prof. Denis Zorin (NYU, Auditor)
Prof. Yann LeCun (NYU, Auditor)

Date: Monday, April 15, 2013
Time: 11:30 am
Room: 719 Broadway Room 1221

Title: Low-level Image Priors and Laplacian Preconditioners for Applications in Computer Graphics and Computational Photography


In the first part of this thesis, we develop novel image priors and efficient algorithms for image denoising and deconvolution applications. Our priors and algorithms enable fast, high-quality restoration of images corrupted by noise or blur. In the second part, we develop effective preconditioners for Laplacian matrices. Such matrices arise in a number of computer graphics and computational photography problems such as image colorization, tone mapping and geodesic distance computation on 3D meshes.

The first prior we develop is a spectral prior which models correlations between different spectral bands. We introduce a prototype camera and flash system, used in conjunction with the spectral prior, to enable taking photographs at very low light levels. Our second prior is a sparsity-based measure for blind image deconvolution. This prior gives lower costs to sharp images than blurred ones, enabling the use simple and efficient Maximum a-Posteriori algorithms.

We develop a new algorithm for the non-blind deconvolution problem. This enables extremely fast deconvolution of images blurred by a known blur kernel. Our algorithm uses Fast Fourier Transforms and Lookup Tables to achieve real-time deconvolution performance with non convex gradient-based priors. Finally, for certain image restoration problems with no clear formation model, we demonstrate how learning a direct mapping between original/corrupted patch pairs enables effective restoration.

We develop multi-level preconditioners to solve discrete Poisson equations. Existing multilevel preconditioners have two major drawbacks: excessive bandwidth growth at coarse levels; and the inability to adapt to problems with highly varying coefficients. Our approach tackles both these problems by introducing sparsification and compensation steps at each level. We interleave the selection of fine and coarse-level variables with the removal of weak connections between potential fine-level variables (sparsification) and compensate for these changes by strengthening nearby connections. By applying these operations before each elimination step and repeating the procedure recursively on the resulting smaller systems, we obtain highly efficient schemes. The construction is linear in time and memory. Numerical experiments demonstrate that our new schemes outperform state of the art methods, both in terms of operation count and wall-clock time, over a range of 2D and 3D problems.