计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2013年
7期
270-273
,共4页
图像去噪%字典学习%贝叶斯模型%稀疏表示%正则化%高斯噪声
圖像去譟%字典學習%貝葉斯模型%稀疏錶示%正則化%高斯譟聲
도상거조%자전학습%패협사모형%희소표시%정칙화%고사조성
image denoising%dictionary learning%Bayesian model%sparse representation%regularization%Gauss noise
稀疏表示因其具有稀疏性、特征保持性等一些特点而被广泛应用于图像处理等领域，为解决图像处理中的去噪问题，提出一种基于图像特征稀疏表示的贝叶斯去噪模型。利用 K-means 和主成分分析方法计算已分割图像块对应字典的矩阵系数，采用正则化约束条件，迭代计算获取的图像字典与原始图像字典之间的差距，优化噪声图片稀疏特征表示的字典，直到达到优化条件。实验结果表明，与传统的离散余弦变换去噪模型相比，该模型的峰值信噪比较高，随着噪声的不断提高，与噪声图像峰值信噪比的差距也越来越大，且图像失真较少。
稀疏錶示因其具有稀疏性、特徵保持性等一些特點而被廣汎應用于圖像處理等領域，為解決圖像處理中的去譟問題，提齣一種基于圖像特徵稀疏錶示的貝葉斯去譟模型。利用 K-means 和主成分分析方法計算已分割圖像塊對應字典的矩陣繫數，採用正則化約束條件，迭代計算穫取的圖像字典與原始圖像字典之間的差距，優化譟聲圖片稀疏特徵錶示的字典，直到達到優化條件。實驗結果錶明，與傳統的離散餘絃變換去譟模型相比，該模型的峰值信譟比較高，隨著譟聲的不斷提高，與譟聲圖像峰值信譟比的差距也越來越大，且圖像失真較少。
희소표시인기구유희소성、특정보지성등일사특점이피엄범응용우도상처리등영역，위해결도상처리중적거조문제，제출일충기우도상특정희소표시적패협사거조모형。이용 K-means 화주성분분석방법계산이분할도상괴대응자전적구진계수，채용정칙화약속조건，질대계산획취적도상자전여원시도상자전지간적차거，우화조성도편희소특정표시적자전，직도체도우화조건。실험결과표명，여전통적리산여현변환거조모형상비，해모형적봉치신조비교고，수착조성적불단제고，여조성도상봉치신조비적차거야월래월대，차도상실진교소。
For the sparse characteristic and maintaining features characteristic, the sparse representation is widely used in image processing. To solve the problem of image denoising in the area of image processing, this paper proposes a new Bayesian denoising model based on image feature sparse representation. The model uses the K-means and Principal Component Analysis(PCA) method to obtain the coefficients of dictionary for sparse representation solutions of image patches. The coefficients solutions are used to train the dictionary with regularized optimization. The alternating minimizations are kept between above two steps until the difference between the image dictionary and the source image dictionary satisfied a convergence criterion. It restores the denoising image under the MAP model with that dictionary. Experimental results show that the higher Peak Signal to Noise Ratio(PSNR) value than the source noised images with the increase of imposed noise into clean images, comparing to the initialization with Discrete Cosine Transform(DCT).
