CAJ | 학술논문

现有的图像去噪算法大多假设图像为分段平滑信号,通过滤除图像中的振动分量达到去噪的效果.如果将这类方法应用于纹理图像,则会导致图像细节信息的损失.该文针对保留图像细节的问题,提出了基于混合线性模型的去噪方法.新方法不假设图像分段平滑,仅假设图像具有自相似性,利用图像的相似性区分图像信号与噪声.文中使用统计学习的方法对图像区域进行聚类,并得到图像主成份,以主成份作为信号分量重组图像,从而对纹理图像取得很好的去噪效果.
현유적도상거조산법대다가설도상위분단평활신호,통과려제도상중적진동분량체도거조적효과.여과장저류방법응용우문리도상,칙회도치도상세절신식적손실.해문침대보류도상세절적문제,제출료기우혼합선성모형적거조방법.신방법불가설도상분단평활,부가설도상구유자상사성,이용도상적상사성구분도상신호여조성.문중사용통계학습적방법대도상구역진행취류,병득도도상주성빈,이주성빈작위신호분량중조도상,종이대문리도상취득흔호적거조효과.
Most of previous image denoising method assume image signal is piecewise smooth. They suppress oscillating patterns in the image to denoise. This kind of method remove high-fre-quency signal from texture image. This paper proposes a hybrid linear model based image denois-ing method in order to preserve texture signal. The new method doesn't use piecewise smooth-ness assumption. Supposing image signal is self-similar while noise is not. Statistical learning al-gorithm is used to cluster the image regions and extract the principle component. The principle component is used as the image signal to form the denoised image, so as to preserve most detail signal of texture image.