کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
562800 875439 2012 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation
چکیده انگلیسی

In this paper, we propose a new three-stage model for multiplicative noise removal. In the first stage, sparse and redundant representation is used to approximate the log-image. The K-SVD algorithm is used to train a redundant dictionary, which can describe the log-image sparsity. Then in the second stage, we use the total variation (TV) method to amend the image obtained. At last, via an exponential function and bias correction, the result is transformed back from the log-domain to the real one. Our method combines the advantages of sparse and redundant representation over trained dictionary and TV method. Experimental results show that the new model is more effective to filter out multiplicative noise than the state-of-the-art models.


► We give a new model for multiplicative noise removal.
► We give the new algorithm for our model.
► Our method might be preferable over other methods at present.
► Experimental results show that our algorithm is simple and efficient for multiplicative noise removal.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 92, Issue 6, June 2012, Pages 1536–1549
نویسندگان
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