Content-matched geometric filtering of image subbands for edge-preserving noise reduction

L. Alparone, A. Garzelli
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引用次数: 1

Abstract

The idea of the present scheme is to apply a directional version of geometric filter (complementary-hull) to the different sub-bands into which the noisy image is decomposed by the S-transform, a dyadic Haar wavelet yielding integer valued coefficients. The hull algorithm is applied only on the direction(s) along which the signal is more structured. The number of iterations is adjusted to the SNR of the sub-bands, so as to preserve spatial details to the largest extent. Comparisons with the standard geometric filter are presented for images affected by synthetic multiplicative (speckle) noise. Results are pretty superior to those achieved without multiresolution context, both visually and in terms of SNR.
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图像子带的内容匹配几何滤波保边降噪
本方案的思想是将几何滤波器的方向版本(互补壳)应用于不同的子带,其中噪声图像被s变换分解,s变换是一种产生整数值系数的二进Haar小波。船体算法只适用于信号更结构化的方向。迭代次数根据子带信噪比进行调整,最大程度地保留空间细节。对受合成乘性(散斑)噪声影响的图像,与标准几何滤波器进行了比较。无论在视觉上还是信噪比方面,结果都比没有多分辨率上下文的结果要好得多。
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Computer Analysis of Images and Patterns: 19th International Conference, CAIP 2021, Virtual Event, September 28–30, 2021, Proceedings, Part I Computer Analysis of Images and Patterns: 19th International Conference, CAIP 2021, Virtual Event, September 28–30, 2021, Proceedings, Part II Computer Analysis of Images and Patterns: CAIP 2019 International Workshops, ViMaBi and DL-UAV, Salerno, Italy, September 6, 2019, Proceedings Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings, Part I Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings, Part II
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