Los Alamos National Laboratory
Phone| Search
T-5 HomeResearchPublications › hu-2014-task
› Contact › People › Research
› Projects › Highlights › Publications
› Jobs › Visitor Info

Cite Details

Huiyi Hu, Brendt Wohlberg and Rick Chartrand, "Task-driven dictionary learning for inpainting", in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Florence, Italy), doi:10.1109/ICASSP.2014.6854260, pp. 3543-3547, May 2014

Abstract

Several approaches used for inpainting of images take advantage of sparse representations. Some of these seek to learn a dictionary that will adapt the sparse representation to the available data. A further refinement is to adapt the learning process to the task itself. In this paper, we formulate a task-driven approach to inpainting as an optimization problem, and derive an algorithm for solving it. We demonstrate via numerical experiments that a purely task-driven approach gives superior results to other dictionary-learning approaches.

BibTeX Entry

@inproceedings{hu-2014-task,
author = {Huiyi Hu and Brendt Wohlberg and Rick Chartrand},
title = {Task-driven dictionary learning for inpainting},
year = {2014},
month = May,
urlpdf = {http://math.lanl.gov/Research/Publications/Docs/hu-2014-task.pdf},
booktitle = {Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
address = {Florence, Italy},
doi = {10.1109/ICASSP.2014.6854260},
pages = {3543-3547}
}