Low-Complexity Regularization Algorithms for Image Deblurring

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Image restoration problems deal with images in which information has been degraded by blur or noise. In practice, the blur is usually caused by atmospheric turbulence, motion, camera shake, and several other mechanical or physical processes. In this study, we present two regularization algorithms for the image deblurring problem. We first present a new method based on solving a regularized least-squares (RLS)problem. This method is proposed to find a near-optimal value of the regularization parameter in the RLS problems. Experimental results on the non-blind image deblurring problem are presented. In all experiments, comparisons are made with three benchmark methods. The results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and structural similarity, as well as the visual quality of the deblurred images. To reduce the complexity of the proposed algorithm, we propose a technique based on the bootstrap method to estimate the regularization parameter in low and high-resolution images. Numerical results show that the proposed technique can effectively reduce the computational complexity of the proposed algorithms. In addition, for some cases where the point spread function (PSF) is separable, we propose using a Kronecker product so as to reduce the computations. Furthermore, in the case where the image is smooth, it is always desirable to replace the regularization term in the RLS problems by a total variation term. Therefore, we propose a novel method for adaptively selecting the regularization parameter in a so-called square root regularized total variation (SRTV). Experimental results demonstrate that our proposed method outperforms the other benchmark methods when applied to smooth images in terms of PSNR, SSIM and the restored image quality. In this thesis, we focus on the non-blind image deblurring problem, where the blur kernel is assumed to be known. However, we developed algorithms that also work in the blind image deblurring. Experimental results show that our proposed methods are robust enough in the blind deblurring and outperform the other benchmark methods in terms of both output PSNR and SSIM values.

روابط وملفات

التعريف والنوع

رقم الوثيقة

edb3c0db-1123-4984-bd5a-eb26b2ddea12

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

1699701_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":" Tareq Al-Naffouri","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Abdulrahman Mubarak Alanazi","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

English

المصدر والدورية

اسم المصدر

Low-Complexity Regularization Algorithms for Image Deblurring

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Blind deblurring , Bootstrap , Least squares , Perturbation , Regularized Total Variation

إشراف وإعداد

الإشراف

Tareq Al-Naffouri

الإعداد

Abdulrahman Mubarak Alanazi

الاقتباسات الببليوغرافية