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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

- Publicized

- 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},}

Copy

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 -