The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pemulihan imej buta untuk kabur gerakan bukan linear dengan fungsi hamparan titik tidak seragam berdasarkan berbilang versi kabur bagi pemandangan yang sama dicadangkan. Pemulihan dianggap secara berasingan sebagai masalah pengenalan dan penyahkonvolusi. Dalam proses pengecaman yang dicadangkan, kesukaran pengenalan diperkenalkan untuk menyusun susunan pengenalan kabur. Imej kabur dengan kesukaran pengenalan paling rendah pada mulanya dikenal pasti dengan menggunakan skema berasaskan imej tunggal. Kemudian, imej lain dikenal pasti berdasarkan hubungan lilitan silang antara setiap pasangan imej kabur. Di samping itu, skim maklum balas berulang digunakan untuk menambah baik keputusan pengenalan. Untuk proses penyahkonvolusi, skema penyesuaian spatial menggunakan titik penamat optimum serantau diubah suai daripada skema penyahkonvolusi lelaran konvensional. Imej-imej diuraikan kepada sub-rantau berdasarkan kelancaran. Titik penamat optimum serantau ditugaskan secara bebas untuk menyekat hingar di kawasan licin dan menajamkan imej di kawasan tegang. Titik penamat yang optimum untuk setiap rantau diputuskan dengan mempertimbangkan ralat percanggahan. Contoh pemulihan imej kabur dunia simulasi dan sebenar dieksperimen untuk menunjukkan prestasi kaedah yang dicadangkan.
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Salinan
Karn PATANUKHOM, Akinori NISHIHARA, "Multiple-Image-Based Restoration for Motion Blur with Non-uniform Point Spread Function" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 8, pp. 1924-1934, August 2008, doi: 10.1093/ietfec/e91-a.8.1924.
Abstract: A blind image restoration for non-linear motion blurs with non-uniform point spread functions based on multiple blurred versions of a same scene is proposed. The restoration is separately considered as identification and deconvolution problems. In the proposed identification process, an identification difficulty is introduced to rank an order of blur identification. A blurred image with the lowest identification difficulty is initially identified by using a single-image-based scheme. Then, other images are identified based on a cross convolution relation between each pair of blurred images. In addition, an iterative feedback scheme is applied to improve the identification results. For the deconvolution process, a spatial adaptive scheme using regional optimal terminating points is modified from a conventional iterative deconvolution scheme. The images are decomposed into sub-regions based on smoothness. The regional optimal terminating points are independently assigned to suppress a noise in smooth regions and sharpen the image in edgy regions. The optimal terminating point for each region is decided by considering a discrepancy error. Restoration examples of simulated and real world blurred images are experimented to demonstrate the performance of the proposed method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.8.1924/_p
Salinan
@ARTICLE{e91-a_8_1924,
author={Karn PATANUKHOM, Akinori NISHIHARA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Multiple-Image-Based Restoration for Motion Blur with Non-uniform Point Spread Function},
year={2008},
volume={E91-A},
number={8},
pages={1924-1934},
abstract={A blind image restoration for non-linear motion blurs with non-uniform point spread functions based on multiple blurred versions of a same scene is proposed. The restoration is separately considered as identification and deconvolution problems. In the proposed identification process, an identification difficulty is introduced to rank an order of blur identification. A blurred image with the lowest identification difficulty is initially identified by using a single-image-based scheme. Then, other images are identified based on a cross convolution relation between each pair of blurred images. In addition, an iterative feedback scheme is applied to improve the identification results. For the deconvolution process, a spatial adaptive scheme using regional optimal terminating points is modified from a conventional iterative deconvolution scheme. The images are decomposed into sub-regions based on smoothness. The regional optimal terminating points are independently assigned to suppress a noise in smooth regions and sharpen the image in edgy regions. The optimal terminating point for each region is decided by considering a discrepancy error. Restoration examples of simulated and real world blurred images are experimented to demonstrate the performance of the proposed method.},
keywords={},
doi={10.1093/ietfec/e91-a.8.1924},
ISSN={1745-1337},
month={August},}
Salinan
TY - JOUR
TI - Multiple-Image-Based Restoration for Motion Blur with Non-uniform Point Spread Function
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1924
EP - 1934
AU - Karn PATANUKHOM
AU - Akinori NISHIHARA
PY - 2008
DO - 10.1093/ietfec/e91-a.8.1924
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2008
AB - A blind image restoration for non-linear motion blurs with non-uniform point spread functions based on multiple blurred versions of a same scene is proposed. The restoration is separately considered as identification and deconvolution problems. In the proposed identification process, an identification difficulty is introduced to rank an order of blur identification. A blurred image with the lowest identification difficulty is initially identified by using a single-image-based scheme. Then, other images are identified based on a cross convolution relation between each pair of blurred images. In addition, an iterative feedback scheme is applied to improve the identification results. For the deconvolution process, a spatial adaptive scheme using regional optimal terminating points is modified from a conventional iterative deconvolution scheme. The images are decomposed into sub-regions based on smoothness. The regional optimal terminating points are independently assigned to suppress a noise in smooth regions and sharpen the image in edgy regions. The optimal terminating point for each region is decided by considering a discrepancy error. Restoration examples of simulated and real world blurred images are experimented to demonstrate the performance of the proposed method.
ER -