کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7924827 | 1512497 | 2018 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Reduction of speckle noise in digital holography by combination of averaging several reconstructed images and modified nonlocal means filtering
ترجمه فارسی عنوان
کاهش نویز خفیف در هولوگرافی دیجیتال با ترکیبی از میانگین چندین عکس بازسازی شده و فیلتر کردن اصطلاح غیر محلی
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کلمات کلیدی
کاهش سرعت، هولوگرافی دیجیتال، پردازش تصویر، سازگاری و اپتیک آماری
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی مواد
مواد الکترونیکی، نوری و مغناطیسی
چکیده انگلیسی
Although the speckle noise in hologram reconstructed image can be effectively suppressed by averaging multiple reconstructions with different speckle patterns, the approach usually needs a lot of the reconstructions and encounters an improvement limit. In this paper, we propose a speckle noise suppression method that combines averaging several reconstructed images and a modified non-local means (MNLM) filtering method. First, the probability density function (PDF) of the averaged speckle field after averaging multiple hologram reconstructions is deduced, then the number of hologram reconstructions for the averaging process is inferred for making the speckle noise distribution approaches Gaussian distribution. Second, considering the statistical model of the multiplicative Gaussian noise, we modify the NLM filtering for effectively removing the residual noise in the averaged image. The experimental results show that the proposed method can reduce the speckle noise more than 90% and achieve nearly speckle-free. Compared with another similar method, the presented method is more effective and feasible to suppress speckle noise and preserve image contrast based on a small number of reconstructions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Optics Communications - Volume 426, 1 November 2018, Pages 9-15
Journal: Optics Communications - Volume 426, 1 November 2018, Pages 9-15
نویسندگان
LeiPing Che, Wen Xiao, Feng Pan, Bin Dong, Zheng Zhong,