Article

Accuracy Comparison between Learning Method and Signal Processing Method Using Iteration for Severely Blur Images

Authors:

Tomio GotoAuthors Info & Claims

DMIP '20: Proceedings of the 2020 3rd International Conference on Digital Medicine and Image Processing

Pages 68 - 73

https://doi.org/10.1145/3441369.3441381

Published: 24 March 2021 Publication History

Abstract

Blurring is one of the most common types of image degradation. When the blurring function (PSF) is unknown and a degraded image is to be recovered, the conventional method requires the estimation of two unknowns, which are the PSF and its ideal image, from a single input image. Thus, the method of performing alternating a PSF estimation and an ideal image estimation processing has been successful. On the other hand, blind image restoration using AI has made remarkable progress in recent years, enabling clearer estimation. In this paper, we compare the conventional iterative method with the AI method, and aim to improve the performance of images containing large blurring, which was not expected in conventional test images.

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Cited By

Kaur RKarmakar GXia F(2022)A Reliable Image Quality Assessment Metric: Evaluation Using Camera ImpactsPattern Recognition and Image Analysis10.1134/S105466182203018X32:3(551-560)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1134/S105466182203018X
Kaur RKarmakar G(2022)Image Quality Assessment Metric Fusing Traditional and Dempster-Shafer TheoryPattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges10.1007/978-3-031-37742-6_37(482-497)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-37742-6_37

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cover image ACM Other conferences

DMIP '20: Proceedings of the 2020 3rd International Conference on Digital Medicine and Image Processing

November 2020

80 pages

ISBN:9781450389044

DOI:10.1145/3441369

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 March 2021

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DMIP '20

DMIP '20: 2020 3rd International Conference on Digital Medicine and Image Processing

November 6 - 9, 2020

Kyoto, Japan

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Cited By

Kaur RKarmakar GXia F(2022)A Reliable Image Quality Assessment Metric: Evaluation Using Camera ImpactsPattern Recognition and Image Analysis10.1134/S105466182203018X32:3(551-560)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1134/S105466182203018X
Kaur RKarmakar G(2022)Image Quality Assessment Metric Fusing Traditional and Dempster-Shafer TheoryPattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges10.1007/978-3-031-37742-6_37(482-497)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-37742-6_37

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