CAJ | 학술논문

针对图像中的高斯噪声干扰，提出一种改进的图像去噪方法．首先利用Curvelet变换将含噪声图像分解成多个子频带，再根据子带系数的高斯分布特性，利用阈值去噪和加权平均滤波相结合的方法对高频子带进行去噪处理，最后利用Curvelet反变换得到去噪后的图像．为了验证该方法的有效性，与传统的硬阈值、软阈值、基于小波变换的方法相比较，实验结果表明，该方法能够获得较好的峰值信噪比和视觉特性，保留较多的细节信息．同时也说明了Curvelet变换比小波变换能够得到更好的去噪效果．
침대도상중적고사조성간우，제출일충개진적도상거조방법．수선이용Curvelet변환장함조성도상분해성다개자빈대，재근거자대계수적고사분포특성，이용역치거조화가권평균려파상결합적방법대고빈자대진행거조처리，최후이용Curvelet반변환득도거조후적도상．위료험증해방법적유효성，여전통적경역치、연역치、기우소파변환적방법상비교，실험결과표명，해방법능구획득교호적봉치신조비화시각특성，보류교다적세절신식．동시야설명료Curvelet변환비소파변환능구득도경호적거조효과．
The purpose of this paper is to study a method of de-noising of images corrupted with additive white Gaussian noise. Firstly, the noisy image is decomposed into many levels to obtain different frequency sub-bands by Curvelet transform. Secondly, the threshold estimation and the weighted average method are used to remove the noisy coefficients according to generalized Gaussian distribution modeling of sub-band coefficients, Ultimately,invert the multi-scale decomposition to reconstruct the de-noised image.Here,to prove the performance of the proposed method,the results are compared with other existent algorithms such as hard and soft threshold based on wavelet. The simulation results on several testing images indicate that the proposed method outperforms the other methods in peak signal to noise ratio and keeps better visual in edges information reservation as well. The results also suggest that Curvelet transform can achieve a better performance than the wavelet transform in image de-noising.