數位影像常會因為不同的原因而受到雜訊的干擾，諸如使用有缺陷的相機感應裝置擷取影像，或是經由劣質的通訊管道進行檔案傳輸等等。一般而言，雜訊會破壞影像結構而嚴重影響到影像的觀賞品質。因此，消除雜訊的方法相對地日益重要。這些方法往往也結合了其他的影像處理技術，例如邊緣偵測、影像切割或物件辨識，而能夠更有效率的減少雜訊數量以及修復影像。
　　在多數消除雜訊的方法中，影像受雜訊干擾的程度扮演了極為關鍵的角色，同時也間接決定了雜訊濾波器的效能。所以在進行消除雜訊的動作之前，通常會先利用其他方法來估測出影像中的雜訊數量。過去在空間域上常被使用來估測雜訊數量的方法，大致上可分為以區塊和以濾波器為基礎的兩種架構，並且各自擁有不同的優缺點。
　　在本篇論文中，我們提出一個利用四元樹影像切割技術並結合上述兩種架構的新型雜訊估測演算法以求得較為可靠的雜訊數量。實驗証明，對於各種不同類型及雜訊數量的影像，此演算法都能有穩定且精準的表現。另外，我們所提出的演算法也可以直接應用在商業化的影像及視訊方面，如數位相機或電視，以獲得更臻完善的視覺效果。

Digital images may be contaminated by noise in various steps such as image acquisition, recording, compression, and transmission through faulty camera sensors or noisy communication channels. In general, noise would corrupt digital images, cause some loss of information details, and degrade the image quality severely. Therefore, noise reduction has become more imperative to suppress noise quantities and recover the corrupted pixels. Moreover, in
order to improve the performance of noise removal, it may combine other image processing steps, like edge detection, image segmentation, or object recognition.
For many noise reduction algorithms, either they are implemented in frequency or in space domain, the noise level is assumed to be known in priori, which is not valid in practical cases and influences the filtering performance
hugely. Due to this fact, noise estimation is a required research topic beforehand to apply actual noise removal operations to noisy image.
Usually, noise estimation algorithms in the spatial domain are categorized in two main approaches: block-based and filter-based. In this thesis, we address an novel algorithm which combines quadtree decomposition with both the two foregoing approaches to estimate the noise level with acceptable computational load. The simulation results show that our algorithm has more outstanding performances in comparison with several existing algorithms for highly noisy and good-quality digital images. Additionally, the proposed
algorithm also could be applied to noise removal in commercial image-based or video-based applications such as digital cameras and television for its performance and simplicity.

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