在現今很多數位影像應用如：醫學影像、掃描與影印技術、或者是人臉辨識等等，往往都很在乎影像的視覺品質。然而，影像經常會在取得或者傳輸的過程中受到雜訊的干擾及汙染，而導致影像的視覺品質降低。為了可以讓這些無可避免的問題有解決的方法，影像去除雜訊的技術便成為在影像處理上非常重要的一個議題。由於去除雜訊的技術常被包在許多需要滿足即時處理的電子產品應用中，所以，一個效果良好的去除雜訊技術是現在廣為討論並且研究的主題。
在本論文中，我們將提出兩個可保留原圖邊緣特性的去除雜訊技術，來分別移除Fix-valued脈衝雜訊與Random-valued脈衝雜訊。針對影像中被偵測為雜訊點的像素值，我們將使用一個3*3大小的工作視窗來找尋其內存在的方向邊緣，用以估計此像素的重建值，而後用此重建值取代掉原值。
實驗證明，本論文提出的方法不管在客觀的數學測量上或者主觀的肉眼觀測都有較好的成效，且由於沒有針對不同的影像採取變動式的工作視窗大小以及疊代處理，因此可以既快速有有效的重建一張雜訊影像。

In digital Image processing, noise removal is a highly demanded area of research. Impulse noise is common in images in which it arises at the time of image acquisition or transmission of images. Impulse noise can be classified into two categories, namely Fix-Valued Noise (Salt-and-Pepper Noise) and Random-Valued Impulse Noise. Most of the nonlinear filters used in removal of impulse noise work in two phases, such as detection followed by filtering only the corrupted pixels, keeping uncorrupted ones intact.
In this thesis, two efficient edge-preserving denoising techniques are proposed to re-move the fix-valued impulse noise and random-valued impulse noise from corrupted im-ages, respectively. Our method can preserve the edge feature efficiently by choosing an optimal edge in the working window, and reconstruct the detected noisy pixels with corre-sponding restored-value. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than state-of –the-art impulse denoising techniques.
Simulation has been carried out at various noise conditions with different standard images. Comparative analysis has been made with existing standard schemes with suitable parameters such as Peak Signal to Noise Ratio (PSNR), SSIM index, fault detection and misses. It has been observed in general that the proposed scheme outperforms its counter-parts at any kinds of noise conditions.

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