Maxima Exploitation for Reference Blurring Function in Motion Deconvolution

Rachel Mabanag CHONG; Toshihisa TANAKA

doi:10.1587/transfun.E94.A.921

Maxima Exploitation for Reference Blurring Function in Motion Deconvolution

Rachel Mabanag CHONG, Toshihisa TANAKA

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The actual blurring function or point spread function (PSF) in an image, in most cases, is similar to a parametric or ideal model. Recently proposed blind deconvolution methods employ this idea for learning during the estimation of PSF. Its dependence on the estimated values may result in ineffective learning when the model is erroneously selected. To overcome this problem, we propose to exploit the image maxima in order to extract a reference point spread function (RPSF). This is only dependent on the degraded image and has a structure that closely resembles a parametric motion blur assuming a known blur support size. Its usage will result in a more stable learning and estimation process since it does not change with respect to iteration or any estimated value. We define a cost function in the vector-matrix form which accounts for the blurring function contour as well as learning towards the RPSF. The effectiveness of using RPSF and the proposed cost function under various motion directions and support sizes will be demonstrated by the experimental results.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E94-A No.3 pp.921-928

Publication Date: 2011/03/01

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Online ISSN: 1745-1337

DOI: 10.1587/transfun.E94.A.921

Type of Manuscript: PAPER

Category: Digital Signal Processing

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Rachel Mabanag CHONG, Toshihisa TANAKA, "Maxima Exploitation for Reference Blurring Function in Motion Deconvolution" in IEICE TRANSACTIONS on Fundamentals, vol. E94-A, no. 3, pp. 921-928, March 2011, doi: 10.1587/transfun.E94.A.921.
Abstract: The actual blurring function or point spread function (PSF) in an image, in most cases, is similar to a parametric or ideal model. Recently proposed blind deconvolution methods employ this idea for learning during the estimation of PSF. Its dependence on the estimated values may result in ineffective learning when the model is erroneously selected. To overcome this problem, we propose to exploit the image maxima in order to extract a reference point spread function (RPSF). This is only dependent on the degraded image and has a structure that closely resembles a parametric motion blur assuming a known blur support size. Its usage will result in a more stable learning and estimation process since it does not change with respect to iteration or any estimated value. We define a cost function in the vector-matrix form which accounts for the blurring function contour as well as learning towards the RPSF. The effectiveness of using RPSF and the proposed cost function under various motion directions and support sizes will be demonstrated by the experimental results.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.921/_p

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@ARTICLE{e94-a_3_921,
author={Rachel Mabanag CHONG, Toshihisa TANAKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Maxima Exploitation for Reference Blurring Function in Motion Deconvolution},
year={2011},
volume={E94-A},
number={3},
pages={921-928},
abstract={The actual blurring function or point spread function (PSF) in an image, in most cases, is similar to a parametric or ideal model. Recently proposed blind deconvolution methods employ this idea for learning during the estimation of PSF. Its dependence on the estimated values may result in ineffective learning when the model is erroneously selected. To overcome this problem, we propose to exploit the image maxima in order to extract a reference point spread function (RPSF). This is only dependent on the degraded image and has a structure that closely resembles a parametric motion blur assuming a known blur support size. Its usage will result in a more stable learning and estimation process since it does not change with respect to iteration or any estimated value. We define a cost function in the vector-matrix form which accounts for the blurring function contour as well as learning towards the RPSF. The effectiveness of using RPSF and the proposed cost function under various motion directions and support sizes will be demonstrated by the experimental results.},
keywords={},
doi={10.1587/transfun.E94.A.921},
ISSN={1745-1337},
month={March},}

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TY - JOUR
TI - Maxima Exploitation for Reference Blurring Function in Motion Deconvolution
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 921
EP - 928
AU - Rachel Mabanag CHONG
AU - Toshihisa TANAKA
PY - 2011
DO - 10.1587/transfun.E94.A.921
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2011
AB - The actual blurring function or point spread function (PSF) in an image, in most cases, is similar to a parametric or ideal model. Recently proposed blind deconvolution methods employ this idea for learning during the estimation of PSF. Its dependence on the estimated values may result in ineffective learning when the model is erroneously selected. To overcome this problem, we propose to exploit the image maxima in order to extract a reference point spread function (RPSF). This is only dependent on the degraded image and has a structure that closely resembles a parametric motion blur assuming a known blur support size. Its usage will result in a more stable learning and estimation process since it does not change with respect to iteration or any estimated value. We define a cost function in the vector-matrix form which accounts for the blurring function contour as well as learning towards the RPSF. The effectiveness of using RPSF and the proposed cost function under various motion directions and support sizes will be demonstrated by the experimental results.
ER -