人類自從真空管發明以來，隨著時代的進步，演變至電晶體與半導體做為人類主要運算的工具。而後隨著電腦演算能力的強大，各式各樣的應用應運而生。在影像方面也因為計算機功能與儲存裝置的容量日益強大，而將傳統底片逐步取代。為了滿足影像應用上的需求，影像品質的改善技術就顯得相當重要。
本論文在此提出一個結合效率與可用性的方法，並應用模糊理論(Fuzzy Theory)，做為一個非線性的濾波器來消除受到脈衝雜訊影響的影像。此方法對於隨機大小的脈衝雜訊與高污染度的影像有著不錯的處理結果。本文所提及的濾波方式使用適應性中值濾波器(Adaptive Median Filter)與模糊理論做為結合，成為適應性中值模糊權重疊代濾波器(Adaptive Median Fuzzy Weighted Recursive Filter)，針對受到不同程度污染的影像做為處理。最後根據實驗比較不同的濾波器濾波結果，可知道本文所使用的方法可以有效的消除低污染度影像與高污染度影像的隨機脈衝雜訊，並保持該影像的完整與細節。

Nowadays, human’s computing technology is progressed from vacuum tube to transmitter and semiconductor. The quantity and quality of computing technology is faster and diverse. Lots of applications depending on computers, especially for mechanical visual technology, are used to replace older films. Since storage devices and CMOS are recently cheaper than before, images can be saved as high-quality digital data. In real world, when an image is transformed from one place to another by a digital technology, the image may be corrupted due to imposed noises like impulse noises.
In literature, there are many algorithms to process corrupted images. A well-known approach is called the median filter, which also refers to the standard filter because lots of moderate filters are developed and based on it. In this thesis, a new approach called the Adaptive Median Fuzzy Weighted Recursive Filter (AMFWRF) is proposed to enhance image filtering and obtain high-quality images. Several experiments will be performed by the AMFWRF and some traditional filters, including the Standard Median Filter (SMF), the Center Weighted Median Filter (CWMF), the Tri-State Median Filter (TSMF), the Adaptive Median Filter (AMF), and the Adaptive Fuzzy Multilevel Filter (AFMF). The tested images are those with different corrupted-level impulse noises.

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