數位影像雜訊的去除在影像處理中是一個相當重要的課題，在早期提出的有貝氏與其他逼近的演算法來修復被雜訊破壞的影像，近來，有一個被稱為雙邊過濾演算法的方法被提出來，這個方法可以在去除雜訊時保留更多影像細節。這是利用中心像素與周圍像素的距離差與亮度值差去計算範圍內的加權平均值，然而使用者在運用這個演算法時，必須去自行找尋合適的對應參數。在這篇論文中，我們提出了一個藉由估算影像雜訊程度，來自動產生合適的對應參數的演算法，藉由比較峰值訊號雜訊比的實驗，我們可以得到比手動調整參數與前人提出的參數還要好的結果，我們在論文中也加入探討各種色域，消除色彩雜訊與過濾範圍大小對影像的影響，最終我們將雙邊過濾演算法實作成硬體，並配合我們提出的演算法，產生一個軟硬體共同運算的系統，我們運用聯電90製程，設計出第一個運用在即時影片的適應式雙邊濾波器，每秒速度可處理180張解析度為720x480的即時影片。

Additive noise removal is an important and practical problem in image processing. Bayesian and other approaches have been proposed to restore the corrupted images. More methods are proposed to preserve signal details while removing noise such as the bilateral filtering. This filter is a weighted average of the local spatial and range distance over a set of data. However, users have to find acceptable two parameters σS and σR in bilateral filters manually by their experience. In this paper, we propose an algorithm which can generate two parameters automatically by estimating standard deviation in noisy images. We also apply this algorithm on images with large additive Gaussian noise. Experimental results compared by PSNR show that our algorithm is better than using manual parameters. We also show that the effect of the domain transform, color noise and window size on the noise removal. We develop a software and hardware based system for this adaptive bilateral filtering. The adaptive parameter estimation algorithm is done in the software and the bilateral filtering is in the hardware. Based on UMC 90 nm process, we design and implement the first real-time bilateral filter that can process 180 frames of size 720x480 per second.

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