基于非下采樣Contourlet變換自適應(yīng)閾值圖像去噪方法研究

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摘要： 圖像去噪是圖像增強(qiáng)、分割、識(shí)別等后續(xù)圖像處理的必要環(huán)節(jié)，Contourlet去噪算法和基于非下采樣Contourlet變換自適應(yīng)閾值圖像去噪方法日益受到學(xué)者的關(guān)注。本文闡述了基于多尺度多方向圖像去噪算法的原理、方法和實(shí)現(xiàn)過程，對(duì)Contourlet和非下采樣Contourlet理論做了詳細(xì)講解并對(duì)Contourlet和非下采樣自適應(yīng)閾值去噪算法進(jìn)行了分析，在開發(fā)軟件MATLAB7.0進(jìn)行了仿真。本文重點(diǎn)分析基于非下采樣Contourlet變換自適應(yīng)閾值圖像去噪方法。首先對(duì)噪聲圖像進(jìn)行非下采樣Contourlet變換，得到各個(gè)尺度各個(gè)方向子帶的系數(shù)，再根據(jù)該系數(shù)的能量自適應(yīng)地調(diào)整去噪閾值。實(shí)驗(yàn)表明，與Contourlet多尺度閾值去噪、Contourlet自適應(yīng)閾值去噪相比，該方法在保留圖像邊緣細(xì)節(jié)的同時(shí)，能提高圖像的PSNR值, 減少了Gibbs現(xiàn)象。

關(guān)鍵詞： Contourlet變換 非下采樣Contourlet變換 圖像去噪 閾值

Research of Image De-Noising Algorithm Using Adaptive Threshold Based on Non Subsampled Contourlet Transform
Abstract ：Image denoising is an essential process for image enhancement, segmentation and recognition. Recently the denoising method based on Contourlet Transform and nonsubsampled contourlet transform is becoming more and more attention by scholars. This paper describes the image denoising theory based on multi-scale and multi-directions transform, introduces the denoising method based on the wavelet and contourlet transform adaptive threshold algorithm, and stimulate in matlab7.0, the results were satisfactory. A new adaptive method of image de-noising based on NonSubsampled Contourlet Transform (NSCT) is importantly presented. The noised image is firstly decomposed into a set of multiscale and multidirectional frequency subbands by NSCT, the threshold is adapted automatically according to the energy of subband coefficients. Comparing with the multi-scale threshold using contourlet transform and using adaptive threshold based on contourlet transform , the simulation results show that the performance of this method is superior in psnr, meanwhile it can maintain the image edge and reduce the Gibbs phenomena.

Keywords ：Contourlet Transform onsubsampled contourlet transform Digital Image Process threshold

目 錄
第一章 緒論 1
1.1 圖像去噪的基本概念 1
1.2 圖像去噪研究的背景、現(xiàn)狀及其意義 2
1.3 課題研究的背景、意義及其發(fā)展 3
第二章 傳統(tǒng)的圖像去噪算法 6
2.1 基于小波分析的圖像去噪 6
2.2 基于多尺度幾何分析的圖像去噪 8
第三章 Contourlet去噪變換方法 11
3.1 基于Contourlet的圖像去噪的內(nèi)容 11
3.2 基于Contourlet的圖像去噪的仿真與結(jié)果 11
第四章 非下采樣Contourlet變換去噪方法 18
4.1 非下采樣 Contourlet 變換 18
4.1.1 非下采樣 Contourlet 變換的內(nèi)容 18
4.1.2 NSCT自適應(yīng)閾值去噪原理 20
4.2 基于非下采樣Contourlet的圖像去噪的仿真與結(jié)果分析 22
4.2.1 對(duì)圖像NSCT 變換去噪仿真 22
4.2.2 對(duì)圖像進(jìn)行NSCT變換去噪仿真結(jié)果分析 23
第五章 總結(jié) 26
5.1 結(jié)論 26
5.2 展望 27
致謝 29
參考文獻(xiàn) 30