CAJ | 학술논문

针对小波图像去噪方法中使用的NeighShrink方法,本文提出了一种有效的保护图像边缘的图像去噪算法.主要改进了NeighShrink方法中固定的邻域范围,根据图像自身的性质,自适应分割成不同的邻域对图像进行去噪处理;并进一步结合小波层内相关性,对各个不规则邻域加上固定的窗口,选择了几何距离更为接近且在同一不规则邻域内的系数,以完善NeighShrink方法.该算法采取平稳小波对含噪图像进行分解,以保持相位不变性,并对低频子带利用脉冲耦合神经网络模型进行图像分割,按照一定的规则将性质相似的像素点相接,得到原图像分割后的信息.在处理过程中利用得到的分割信息对边缘予以保护.实验结果表明,该方法在降低了图像噪声的同时又尽可能地保留了图像的边缘信息,是一种有效的去噪方法.
침대소파도상거조방법중사용적NeighShrink방법,본문제출료일충유효적보호도상변연적도상거조산법.주요개진료NeighShrink방법중고정적린역범위,근거도상자신적성질,자괄응분할성불동적린역대도상진행거조처리;병진일보결합소파층내상관성,대각개불규칙린역가상고정적창구,선택료궤하거리경위접근차재동일불규칙린역내적계수,이완선NeighShrink방법.해산법채취평은소파대함조도상진행분해,이보지상위불변성,병대저빈자대이용맥충우합신경망락모형진행도상분할,안조일정적규칙장성질상사적상소점상접,득도원도상분할후적신식.재처리과정중이용득도적분할신식대변연여이보호.실험결과표명,해방법재강저료도상조성적동시우진가능지보류료도상적변연신식,시일충유효적거조방법.
For NeighShrink method used in the image de-noising, a new image de-noising algorithm is proposed to keep image edges more effectively, and it mainly improve the domain of NeighShrink which is fixed. The new method can segment the image into many domains adaptively to de-noise the images. Furthermore, combined with wavelet correlation in the same layer, we get various irregular neighborhoods with a fixed window, and choose the coefficients which have closer geometric distance and are in the same irregular neighborhood to improve NeighShrink. This method decomposes noisy images with stationary wavelet transform to keep phase invariance. Then, in accordance with special rules, it segments the low frequency sub-band by using Pulse Coupled Neural Networks (PCNN) model, and then gets the approximate information. And the edge information will be protected during the de-noising process. A better restoration of images is demonstrated in the results of experiments, with detail of images kept as well as image noises decreasing.