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
Penyahkaburan gerakan untuk imej bising dan kabur adalah masalah yang sukar dan asas dalam komuniti pemprosesan imej. Masalahnya adalah buruk kerana banyak pasangan imej terpendam dan kernel kabur yang berbeza boleh menyebabkan imej kabur yang sama, dan dengan itu, pengoptimuman masalah ini masih belum diselesaikan. Untuk mengatasinya, kami mempersembahkan kaedah penyahkaburan gerakan yang berkesan untuk imej bising dan kabur berdasarkan struktur yang menonjol dan ekor berat dipacu data sebelum kecerunan dipertingkatkan. Khususnya, pertama, kami menggunakan denoising sebagai praproses untuk mengalih keluar hingar imej input, dan kemudian memulihkan tepi yang kuat untuk anggaran kernel yang tepat. Imej terawal berasaskan saluran melampau (saluran gelap sebelum dan saluran terang sebelumnya) kerana pengetahuan pelengkap yang jarang dieksploitasi untuk mengekstrak struktur yang menonjol. Kedekatan tinggi struktur yang diekstrak dengan struktur imej yang jelas boleh diperoleh melalui penalaan parameter fungsi pengekstrakan. Seterusnya, istilah penyepaduan kecerunan imej interim yang dipertingkatkan dan imej yang jelas berekor berat sebelum dicadangkan dan kemudian dibenamkan ke dalam model pemulihan imej, yang mengutamakan imej yang tajam berbanding yang kabur. Sebilangan besar percubaan pada kedua-dua imej sintetik dan kehidupan sebenar mengesahkan keunggulan kaedah yang dicadangkan berbanding algoritma tercanggih, secara kualitatif dan kuantitatif.
Hongtian ZHAO
Shanghai Jiao Tong University
Shibao ZHENG
Shanghai Jiao Tong University
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Salinan
Hongtian ZHAO, Shibao ZHENG, "Joint Extreme Channels-Inspired Structure Extraction and Enhanced Heavy-Tailed Priors Heuristic Kernel Estimation for Motion Deblurring of Noisy and Blurry Images" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 12, pp. 1520-1528, December 2020, doi: 10.1587/transfun.2020SMP0008.
Abstract: Motion deblurring for noisy and blurry images is an arduous and fundamental problem in image processing community. The problem is ill-posed as many different pairs of latent image and blur kernel can render the same blurred image, and thus, the optimization of this problem is still unsolved. To tackle it, we present an effective motion deblurring method for noisy and blurry images based on prominent structure and a data-driven heavy-tailed prior of enhanced gradient. Specifically, first, we employ denoising as a preprocess to remove the input image noise, and then restore strong edges for accurate kernel estimation. The image extreme channels-based priors (dark channel prior and bright channel prior) as sparse complementary knowledge are exploited to extract prominent structure. High closeness of the extracted structure to the clear image structure can be obtained via tuning the parameters of extraction function. Next, the integration term of enhanced interim image gradient and clear image heavy-tailed prior is proposed and then embedded into the image restoration model, which favors sharp images over blurry ones. A large number of experiments on both synthetic and real-life images verify the superiority of the proposed method over state-of-the-art algorithms, both qualitatively and quantitatively.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020SMP0008/_p
Salinan
@ARTICLE{e103-a_12_1520,
author={Hongtian ZHAO, Shibao ZHENG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Joint Extreme Channels-Inspired Structure Extraction and Enhanced Heavy-Tailed Priors Heuristic Kernel Estimation for Motion Deblurring of Noisy and Blurry Images},
year={2020},
volume={E103-A},
number={12},
pages={1520-1528},
abstract={Motion deblurring for noisy and blurry images is an arduous and fundamental problem in image processing community. The problem is ill-posed as many different pairs of latent image and blur kernel can render the same blurred image, and thus, the optimization of this problem is still unsolved. To tackle it, we present an effective motion deblurring method for noisy and blurry images based on prominent structure and a data-driven heavy-tailed prior of enhanced gradient. Specifically, first, we employ denoising as a preprocess to remove the input image noise, and then restore strong edges for accurate kernel estimation. The image extreme channels-based priors (dark channel prior and bright channel prior) as sparse complementary knowledge are exploited to extract prominent structure. High closeness of the extracted structure to the clear image structure can be obtained via tuning the parameters of extraction function. Next, the integration term of enhanced interim image gradient and clear image heavy-tailed prior is proposed and then embedded into the image restoration model, which favors sharp images over blurry ones. A large number of experiments on both synthetic and real-life images verify the superiority of the proposed method over state-of-the-art algorithms, both qualitatively and quantitatively.},
keywords={},
doi={10.1587/transfun.2020SMP0008},
ISSN={1745-1337},
month={December},}
Salinan
TY - JOUR
TI - Joint Extreme Channels-Inspired Structure Extraction and Enhanced Heavy-Tailed Priors Heuristic Kernel Estimation for Motion Deblurring of Noisy and Blurry Images
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1520
EP - 1528
AU - Hongtian ZHAO
AU - Shibao ZHENG
PY - 2020
DO - 10.1587/transfun.2020SMP0008
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2020
AB - Motion deblurring for noisy and blurry images is an arduous and fundamental problem in image processing community. The problem is ill-posed as many different pairs of latent image and blur kernel can render the same blurred image, and thus, the optimization of this problem is still unsolved. To tackle it, we present an effective motion deblurring method for noisy and blurry images based on prominent structure and a data-driven heavy-tailed prior of enhanced gradient. Specifically, first, we employ denoising as a preprocess to remove the input image noise, and then restore strong edges for accurate kernel estimation. The image extreme channels-based priors (dark channel prior and bright channel prior) as sparse complementary knowledge are exploited to extract prominent structure. High closeness of the extracted structure to the clear image structure can be obtained via tuning the parameters of extraction function. Next, the integration term of enhanced interim image gradient and clear image heavy-tailed prior is proposed and then embedded into the image restoration model, which favors sharp images over blurry ones. A large number of experiments on both synthetic and real-life images verify the superiority of the proposed method over state-of-the-art algorithms, both qualitatively and quantitatively.
ER -