Blind image deconvolution using specified HPF for feature extraction and conjugate gradient method in frequency domain

Takanori Fujisawa, Masaaki Ikehara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Image deconvolution is the task to recover the image information that was lost by taking photos with blur motion. Especially blind image deconvolution requires no prior informations other than the blurred image. This problem is seriously ill-posed and an additional operation is required such as extracting image features. In this paper, we present a blind image deconvolution framework using a specified highpass filter (HPF) for feature extraction to estimate a blur kernel. This problem can consider the kernel estimation in the region where salient edges are not present and improve the quality of the estimated kernel. Our approach also accelerates the deconvolution process by utilizing a conjugate gradient method in a frequency domain. This process eliminates costly convolution operations from the iterative updating and reduces the calculation time. Evaluation for 20 test images shows our framework not only performs faster than conventional frameworks but also improves the quality of recovered images.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages2713-2717
Number of pages5
Volume2016-August
ISBN (Electronic)9781467399616
DOIs
Publication statusPublished - 2016 Aug 3
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: 2016 Sep 252016 Sep 28

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
CountryUnited States
CityPhoenix
Period16/9/2516/9/28

Fingerprint

Conjugate gradient method
Deconvolution
Feature extraction
Convolution

Keywords

  • Deblurring
  • Feature Extraction
  • Optimization

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Fujisawa, T., & Ikehara, M. (2016). Blind image deconvolution using specified HPF for feature extraction and conjugate gradient method in frequency domain. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (Vol. 2016-August, pp. 2713-2717). [7532852] IEEE Computer Society. https://doi.org/10.1109/ICIP.2016.7532852

Blind image deconvolution using specified HPF for feature extraction and conjugate gradient method in frequency domain. / Fujisawa, Takanori; Ikehara, Masaaki.

2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. Vol. 2016-August IEEE Computer Society, 2016. p. 2713-2717 7532852.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Fujisawa, T & Ikehara, M 2016, Blind image deconvolution using specified HPF for feature extraction and conjugate gradient method in frequency domain. in 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. vol. 2016-August, 7532852, IEEE Computer Society, pp. 2713-2717, 23rd IEEE International Conference on Image Processing, ICIP 2016, Phoenix, United States, 16/9/25. https://doi.org/10.1109/ICIP.2016.7532852
Fujisawa T, Ikehara M. Blind image deconvolution using specified HPF for feature extraction and conjugate gradient method in frequency domain. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. Vol. 2016-August. IEEE Computer Society. 2016. p. 2713-2717. 7532852 https://doi.org/10.1109/ICIP.2016.7532852
Fujisawa, Takanori ; Ikehara, Masaaki. / Blind image deconvolution using specified HPF for feature extraction and conjugate gradient method in frequency domain. 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. Vol. 2016-August IEEE Computer Society, 2016. pp. 2713-2717
@inproceedings{dfc1b5e45b3f4d7986ba9186cabd7f5b,
title = "Blind image deconvolution using specified HPF for feature extraction and conjugate gradient method in frequency domain",
abstract = "Image deconvolution is the task to recover the image information that was lost by taking photos with blur motion. Especially blind image deconvolution requires no prior informations other than the blurred image. This problem is seriously ill-posed and an additional operation is required such as extracting image features. In this paper, we present a blind image deconvolution framework using a specified highpass filter (HPF) for feature extraction to estimate a blur kernel. This problem can consider the kernel estimation in the region where salient edges are not present and improve the quality of the estimated kernel. Our approach also accelerates the deconvolution process by utilizing a conjugate gradient method in a frequency domain. This process eliminates costly convolution operations from the iterative updating and reduces the calculation time. Evaluation for 20 test images shows our framework not only performs faster than conventional frameworks but also improves the quality of recovered images.",
keywords = "Deblurring, Feature Extraction, Optimization",
author = "Takanori Fujisawa and Masaaki Ikehara",
year = "2016",
month = "8",
day = "3",
doi = "10.1109/ICIP.2016.7532852",
language = "English",
volume = "2016-August",
pages = "2713--2717",
booktitle = "2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - Blind image deconvolution using specified HPF for feature extraction and conjugate gradient method in frequency domain

AU - Fujisawa, Takanori

AU - Ikehara, Masaaki

PY - 2016/8/3

Y1 - 2016/8/3

N2 - Image deconvolution is the task to recover the image information that was lost by taking photos with blur motion. Especially blind image deconvolution requires no prior informations other than the blurred image. This problem is seriously ill-posed and an additional operation is required such as extracting image features. In this paper, we present a blind image deconvolution framework using a specified highpass filter (HPF) for feature extraction to estimate a blur kernel. This problem can consider the kernel estimation in the region where salient edges are not present and improve the quality of the estimated kernel. Our approach also accelerates the deconvolution process by utilizing a conjugate gradient method in a frequency domain. This process eliminates costly convolution operations from the iterative updating and reduces the calculation time. Evaluation for 20 test images shows our framework not only performs faster than conventional frameworks but also improves the quality of recovered images.

AB - Image deconvolution is the task to recover the image information that was lost by taking photos with blur motion. Especially blind image deconvolution requires no prior informations other than the blurred image. This problem is seriously ill-posed and an additional operation is required such as extracting image features. In this paper, we present a blind image deconvolution framework using a specified highpass filter (HPF) for feature extraction to estimate a blur kernel. This problem can consider the kernel estimation in the region where salient edges are not present and improve the quality of the estimated kernel. Our approach also accelerates the deconvolution process by utilizing a conjugate gradient method in a frequency domain. This process eliminates costly convolution operations from the iterative updating and reduces the calculation time. Evaluation for 20 test images shows our framework not only performs faster than conventional frameworks but also improves the quality of recovered images.

KW - Deblurring

KW - Feature Extraction

KW - Optimization

UR - http://www.scopus.com/inward/record.url?scp=85006791115&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006791115&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2016.7532852

DO - 10.1109/ICIP.2016.7532852

M3 - Conference contribution

VL - 2016-August

SP - 2713

EP - 2717

BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings

PB - IEEE Computer Society

ER -