کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
848471 909244 2014 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Split Bregman iteration solution for sparse optimization in image restoration
ترجمه فارسی عنوان
تقسیم برگرمن تکرار راه حل برای بهینه سازی اسپرد در بازسازی تصویر
کلمات کلیدی
ترمیم تصویر، نمایندگی انحصاری و انحصاری، تقسیم برگرمن تکرار، یادگیری دیکشنری
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی (عمومی)
چکیده انگلیسی

It is always a challenging task to develop effective and accurate models for robust image restoration. In this paper, the family of sparse and redundant representation frameworks is considered as an alternative for the above problem. The principle of the family is expatiated on the development and research progress. Two well-known denoising methods are presented and analyzed on their properties. The K-SVD algorithm is an effective method for sparse representation. The iteratively approximate algorithms are always used for the solution of sparse coding operations. Here, a convexification of the l0 norm to the l1 norm is adopted in the implementation of K-SVD method. Then a split Bregman iteration solution is proposed for l1 regularization problems in the performance of the sparse representation of the K-SVD algorithm. The split Bregman iterative method is well studied and fused into the famous K-SVD method. The PSNR (Peak Signal to Noise Ratio) and MSSIM (Mean Structural Similarity) are used to evaluate the performance of those methods. Experimental results on different types of images indicate that our proposed method not only achieve comparable results with the state of art methods, but also make the original method more efficient. Besides, it also provides a valuable and promising reference for image restoration techniques.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Optik - International Journal for Light and Electron Optics - Volume 125, Issue 19, October 2014, Pages 5635–5640
نویسندگان
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