基於人類視覺系統之混合雜訊消除技術

Abstract

雜訊常是嚴重損害影像品質並且破壞重要細節的因素。附加在影像上的雜訊，大體上可以用兩種雜訊模型來模擬表現：突波雜訊及高斯雜訊。本論文提出兩級式的架構，相繼消除混合雜訊影像中非線性的突波雜訊及線性的高斯雜訊。在第一級消除突波雜訊中，基於決策機制的適應性中位數濾波器，應用於消除椒鹽雜訊 (Salt-Pepper noise)，而另一個適應性類神經網路的架構，則用於消除隨機的突波雜訊。然後，我們提出以人類視覺系統為基礎的影像品質增強系統，再補償修改過的圖素。在第二級消除高斯雜訊中，我們提出了線性改良式模糊規則為基礎的濾波器 (MFRB) 消除線性的高斯雜訊，並盡可能的保留影像的邊緣及細節部分。為了考量實際狀況，我們設計了幾個通用的MFRB濾波器來處理各種不同摻雜程度的高斯雜訊。利用所估測到影像雜訊值的大小，我們選擇對應到雜訊摻雜程度最接近的MFRB濾波器，去除影像中的高斯雜訊。根據實驗結果顯示，所提出的方法不論在客觀的評比數據(PSNR)或主觀的視覺感知上，都優於其他所比較的雜訊消除技術。

Noise always significantly damages an image and can corrupt most important details. Two noise models can adequately represent most noise added to images: additive impulse noise and Gaussian noise. In this thesis, we propose a two-stage filtering method to sequentially remove the mixed noises of images corrupted with nonlinear impulse and linear Gaussian noises as well. In the first stage, the decision-based recursive adaptive median filter is applied to remove the Salt-Pepper noise, and an adaptive two-level neural network noise reduction procedure is applied to remove the random-valued noise. Then, an HVS-directed neural-network-based image quality enhancement is applied to compensate the modified pixels. In the second stage, we derive a linear modified fuzzy rule-based (MFRB) filter to remove the linear type Gaussian noise while preserving the image edges and details as much as possible. For practical consideration, we design several sets of universal MFRB filters, to be utilized in correspondence to the estimated values of contaminated Gaussian noise variance in the image. The correspondent MFRB filter closest to the estimated Gaussian noise level will be selected to remove the Gaussian noise of the processed image. According to the experiment results, the proposed method is superior, both quantitatively and visually, compared to several other techniques.