本文選題：圖像降噪 + 塊匹配和三維濾波(BMD)算法�。� 參考：《光電子·激光》2017年06期

【摘要】：針對(duì)經(jīng)典的塊匹配和三維濾波(BM3D)降噪算法中最為核心的噪聲水平(方差)參數(shù)在使用中需要人工手動(dòng)設(shè)置極大影響了降噪效果并限制了它的應(yīng)用,提出了一種新的基于自然場(chǎng)景統(tǒng)計(jì)(NSS)的噪聲水平特征矢量和支持向量回歸(SVR)技術(shù)的快速噪聲水平估計(jì)算法并應(yīng)用于經(jīng)典BM3D算法中,使之轉(zhuǎn)變?yōu)樽赃m應(yīng)降噪算法(Adaptive BM3D)。本文算法首先利用小波變換對(duì)圖像進(jìn)行不同尺度和不同方向的分解,提取各子帶濾波系數(shù)并用通用高斯分布模型(GGD)建模,以模型參數(shù)構(gòu)成反映噪聲圖像噪聲水平的特征矢量;然后用SVR方法在大量噪聲圖像樣本上進(jìn)行訓(xùn)練獲得圖像噪聲水平預(yù)測(cè)模型。實(shí)驗(yàn)表明:改進(jìn)后的ABM3D算法實(shí)際圖像降噪效果比BM3D算法獲得進(jìn)一步提升,并且仍然保持了非常高的執(zhí)行效率,相對(duì)于當(dāng)前各主流算法具有明顯的競(jìng)爭(zhēng)力。
[Abstract]:In the classical block matching and 3D filtering (BM3D) denoising algorithm, the manual setting of the noise level (variance) parameter, which is the core of the classical block matching and three dimensional filtering (BM3D) denoising algorithm, has greatly affected the noise reduction effect and limited its application. A new fast noise level estimation algorithm based on the feature vector and support vector regression (SVR) of natural scene statistics is proposed and applied to the classical BM3D algorithm to transform it into an adaptive noise reduction algorithm (Adaptive BM3D). Firstly, the wavelet transform is used to decompose the image in different scales and in different directions, and the filter coefficients of each subband are extracted and modeled with the general Gao Si distribution model, and the feature vectors reflecting the noise level of the noise image are constructed by the model parameters. Then SVR method is used to train a large number of noise image samples to obtain image noise level prediction model. The experimental results show that the actual image denoising effect of the improved ABM3D algorithm is further improved than that of the BM3D algorithm, and it still maintains a very high execution efficiency, which is obviously competitive compared with the current mainstream algorithms.
【作者單位】： 南昌大學(xué)信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(61662044,61163023,61379018) 國(guó)家級(jí)大學(xué)生雙創(chuàng)項(xiàng)目(201510403030)資助項(xiàng)目
【分類號(hào)】：TN713;TP391.41

【相似文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 嚴(yán)馨葉;基于雙傳聲器藍(lán)牙耳機(jī)的降噪算法研究[D];南京大學(xué);2014年

2 丁淼;基于DSP的CCD降噪電路設(shè)計(jì)和算法研究[D];華南理工大學(xué);2012年

，

本文編號(hào)：2108239