基于模糊集和细分的图像增强方法

Guo Xian Jiu, Jiang Feng Jiao, L. Xiang
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引用次数: 4

摘要

藻类显微图像通常存在大量的噪声和模糊。因此,一种既能去除噪声又能保留细节信息的图像增强算法对藻类显微图像的处理至关重要。提出了一种基于模糊集和细分的图像增强方法。它对藻类显微图像处理是有效的。细分方案在不同细分层之间良好的相似性使得多分辨率分析在分解后的信号与初始图像之间有较好的逼近性。细分方法通过将初始图像分解成低通部分,具有较强的抑制噪声的能力。通过对初始图像的低通部分进行细分,重构图像。然后利用模糊集方法对重构图像进行增强。在模糊化过程中,采用一个特殊的函数作为隶属函数。实验结果证明了该方法对藻类显微镜图像处理的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image enhancement method based on fuzzy set and subdivision
Alga microscopic image usually has a lot of noises and blurs. So a proper image enhancement algorithm which can remove noise and retain detail information is very important for alga microscopic image disposal. In the paper a new image enhancement method based on fuzzy set and subdivision is proposed. It is effective for alga microscopic image disposal. Subdivision scheme's good similarity among different subdivision layers makes the multi-resolution analysis has better approximation between the decomposed signals and the initial image. Subdivision method has strong ability to suppress noise through decomposing the initial image into low pass part. The image can be reconstructed through subdividing the low pass part of the initial image. Then the fuzzy set method is used for enhancement the reconstructed image. A special function is used as membership function in the process of fuzzification. The experimental results demonstrate the effectiveness of the proposed method for alga microscope image diaposal.
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