臺灣博碩士論文加值系統

在影像處理的研究中，去除雜訊一直是一個非常重要的議題，其中又以椒鹽雜訊最為常見，而中值濾波器被公認為在去除椒鹽雜訊的方法中是最為有效的。
在本論文中，提出一個適應性的中值濾波器架構，針對椒鹽雜訊的特性來偵測雜訊，並且使用兩種適應性的window type搭配管線化的排序方法來去除雜訊。
本文將去除雜訊分為兩個步驟，雜訊偵測以及雜訊去除，利用椒鹽雜訊為極大或極小的像素值特性來進行雜訊偵測的動作，將偵測到的雜訊像素進行處理，並且保留圖像中非雜訊的像素值，而在進行雜訊去除時，針對雜訊密度選擇適合的window type進行適應性的處理，在排序方面並非使用常見的氣泡排序法，而是使用管線化的排序架構，以減少比較器的數量、排序時間以及電路面積。
由於本研究所使用的適應性架構，故能執行在1%-90%雜訊密度中，並且保持圖像大部分的細節，實驗結果顯示，本電路不僅能夠執行在高速的電路中，同時也提供了穩定的影像品質。

Referring to previous researches regarding image processing, the elimination of the noise has played an important role, especially the most commonly seen salt-and-pepper noise. Moreover, median filter has been recognized as the most effective method to eliminate noise.
The present study proposed an adaptive median filter architecture, in which the noise-detecting features of salt-and-pepper, and the trait of the two kinds of adaptive window with sorted pipeline to remove noise, were involved.
In the current research, noise removal consisted of two steps, including noise detection and noise filtering. In the first step, either the highest or the smallest value of salt-and-pepper was adopted to detect noise, while the detected noise would be manipulated then, keeping the non-noisy pixel intact in the image. In the process of removing noise, window type, serving as noise filter, was selected based on the density of the noise. Moreover, pipeline sorting was used for the ordering, which reduced the capacity of comparator, decreased the time for ordering, as well as the circuit area.
Since the present study used adaptive architecture, 1% to 90% density of noise was executed without the loss of details of the image. The results showed that the proposed circuit not only could be executed at high speed but also provided stable image quality.

中文摘要 I
英文摘要 II
誌謝 III
目錄 IV
圖目錄 V
表目錄 VII
第一章　緒論 1
第一節　研究背景與動機 1
第二節　研究方法 3
第三節　論文架構 5
第二章　相關研究與文獻探討 6
第一節　快速及高效中值濾波器 7
第二節　節能中值濾波器實作於FPGA 12
第三章　研究方法 19
第一節　椒鹽雜訊 19
第二節　管線化架構 20
第三節　中值濾波器 21
第四節　雜訊比檢測電路 22
第五節　管線化中值濾波器電路 25
第四章　實驗結果 30
第五章　結論 52

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