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Distortion Rectification from Static to Dynamic: A Distortion Sequence Construction Perspective

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Distortion Rectification from Static to Dynamic: A Distortion Sequence Construction Perspective. / Liao, Kang; Lin, Chunyu; Zhao, Yao; Gabbouj, Moncef.

julkaisussa: IEEE Transactions on Circuits and Systems for Video Technology, 06.12.2019.

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Harvard

APA

Liao, K., Lin, C., Zhao, Y., & Gabbouj, M. (2019). Distortion Rectification from Static to Dynamic: A Distortion Sequence Construction Perspective. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/TCSVT.2019.2958199

Vancouver

Liao K, Lin C, Zhao Y, Gabbouj M. Distortion Rectification from Static to Dynamic: A Distortion Sequence Construction Perspective. IEEE Transactions on Circuits and Systems for Video Technology. 2019 joulu 6. https://doi.org/10.1109/TCSVT.2019.2958199

Author

Liao, Kang ; Lin, Chunyu ; Zhao, Yao ; Gabbouj, Moncef. / Distortion Rectification from Static to Dynamic: A Distortion Sequence Construction Perspective. Julkaisussa: IEEE Transactions on Circuits and Systems for Video Technology. 2019.

Bibtex - Lataa

@article{c823d63a955d43169d6f8597f5d20582,
title = "Distortion Rectification from Static to Dynamic: A Distortion Sequence Construction Perspective",
abstract = "Distortion rectification is a fundamental task in the field of computer vision and image processing. Nevertheless, previous methods have regarded distortion rectification as a static problem that learns a mapping function and corrects the distorted image to a unique state. However, this state is generally not the optimal solution, as it would result in an under-rectified or over-rectified structure. In this study, we revisit the classical distortion rectification task with a new perspective and redesign the algorithm, inspired by video processing techniques. Specifically, we regard distortion rectification as a dynamic problem that can be extended to a sequence of different distortion states: the input distorted image (t), under-rectified image (t+1), ideal-rectified image (t+2), and over-rectified image (t+3). We first estimate the residual distortion map (RDM) between the input distorted image and the coarse-rectified (t+1 or t+3) image. Here, RDM indicates the motion difference between two distorted images. Subsequently, the RDM is used to guide the refinement rectification process, aiming to convert the coarse-rectified state into the ideal-rectified state. In addition, the flexible implementation of the proposed refinement process with RDM to improve the rectification results of any method is appealing. The experimental results demonstrate that our method outperforms the state-of-the-art schemes by a significant margin, revealing approximately 40{\%} improvement through quantitative evaluation.",
keywords = "Cameras, Nonlinear distortion, Optical distortion, Task analysis, Feature extraction, Learning systems, Distortion Rectification, Deep Learning, Video Processing, Dynamic Construction",
author = "Kang Liao and Chunyu Lin and Yao Zhao and Moncef Gabbouj",
year = "2019",
month = "12",
day = "6",
doi = "10.1109/TCSVT.2019.2958199",
language = "English",
journal = "IEEE Transactions on Circuits and Systems for Video Technology",
issn = "1051-8215",
publisher = "Institute of Electrical and Electronics Engineers",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Distortion Rectification from Static to Dynamic: A Distortion Sequence Construction Perspective

AU - Liao, Kang

AU - Lin, Chunyu

AU - Zhao, Yao

AU - Gabbouj, Moncef

PY - 2019/12/6

Y1 - 2019/12/6

N2 - Distortion rectification is a fundamental task in the field of computer vision and image processing. Nevertheless, previous methods have regarded distortion rectification as a static problem that learns a mapping function and corrects the distorted image to a unique state. However, this state is generally not the optimal solution, as it would result in an under-rectified or over-rectified structure. In this study, we revisit the classical distortion rectification task with a new perspective and redesign the algorithm, inspired by video processing techniques. Specifically, we regard distortion rectification as a dynamic problem that can be extended to a sequence of different distortion states: the input distorted image (t), under-rectified image (t+1), ideal-rectified image (t+2), and over-rectified image (t+3). We first estimate the residual distortion map (RDM) between the input distorted image and the coarse-rectified (t+1 or t+3) image. Here, RDM indicates the motion difference between two distorted images. Subsequently, the RDM is used to guide the refinement rectification process, aiming to convert the coarse-rectified state into the ideal-rectified state. In addition, the flexible implementation of the proposed refinement process with RDM to improve the rectification results of any method is appealing. The experimental results demonstrate that our method outperforms the state-of-the-art schemes by a significant margin, revealing approximately 40% improvement through quantitative evaluation.

AB - Distortion rectification is a fundamental task in the field of computer vision and image processing. Nevertheless, previous methods have regarded distortion rectification as a static problem that learns a mapping function and corrects the distorted image to a unique state. However, this state is generally not the optimal solution, as it would result in an under-rectified or over-rectified structure. In this study, we revisit the classical distortion rectification task with a new perspective and redesign the algorithm, inspired by video processing techniques. Specifically, we regard distortion rectification as a dynamic problem that can be extended to a sequence of different distortion states: the input distorted image (t), under-rectified image (t+1), ideal-rectified image (t+2), and over-rectified image (t+3). We first estimate the residual distortion map (RDM) between the input distorted image and the coarse-rectified (t+1 or t+3) image. Here, RDM indicates the motion difference between two distorted images. Subsequently, the RDM is used to guide the refinement rectification process, aiming to convert the coarse-rectified state into the ideal-rectified state. In addition, the flexible implementation of the proposed refinement process with RDM to improve the rectification results of any method is appealing. The experimental results demonstrate that our method outperforms the state-of-the-art schemes by a significant margin, revealing approximately 40% improvement through quantitative evaluation.

KW - Cameras

KW - Nonlinear distortion

KW - Optical distortion

KW - Task analysis

KW - Feature extraction

KW - Learning systems

KW - Distortion Rectification

KW - Deep Learning

KW - Video Processing

KW - Dynamic Construction

U2 - 10.1109/TCSVT.2019.2958199

DO - 10.1109/TCSVT.2019.2958199

M3 - Article

JO - IEEE Transactions on Circuits and Systems for Video Technology

JF - IEEE Transactions on Circuits and Systems for Video Technology

SN - 1051-8215

ER -