Fast Deconvolution with Color Constraints on Gradients

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 http://vision.seas.harvard. edu/ccap, and takes roughly 20 seconds to deconvolve a three-channel 1544 1028 color image, on a Linux-based Intel I-3 2.1GHz machine.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2013
Accession Number
ADA581820

Entities

People

  • Ayan Chakrabarti
  • Todd Zickler

Organizations

  • Harvard University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artifacts
  • Boundaries
  • Computer Vision
  • Databases
  • Discontinuities
  • Frequency
  • Gaussian Noise
  • Information Operations
  • Iterations
  • Military Research
  • Noise
  • Optimization
  • Splitting
  • Statistics
  • Test And Evaluation

Readers

  • Image Processing and Computer Vision.
  • Parallel and Distributed Computing.
  • Wave Propagation and Nonlinear Chaotic Dynamics.

Technology Areas

  • Space