Accession Number:

ADA581820

Title:

Fast Deconvolution with Color Constraints on Gradients

Descriptive Note:

Technical rept.

Corporate Author:

HARVARD UNIV CAMBRIDGE MA

Personal Author(s):

Report Date:

2013-01-01

Pagination or Media Count:

5.0

Abstract:

In this report, we describe a fast deconvolution approach for color images that combines a sparse regularization cost on the magnitudes of gradients with constraints on their direction in color space. We form these color constraints in a way that allows retaining the computationally-efficient optimization strategy introduced in recent deconvolution methods based on half-quadratic splitting. The proposed algorithm is capable of handling a different blur kernel in each color channel, and is used for per-layer deconvolution in our paper Depth and Deblurring from a Spectrally-varying Depth-of-Field 1. A MATLAB implementation of this method is available at httpvision.seas.harvard. educcap, and takes roughly 20 seconds to deconvolve a three-channel 1544 1028 color image, on a Linux-based Intel I-3 2.1GHz machine.

Subject Categories:

  • Electrooptical and Optoelectronic Devices
  • Theoretical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE