一种基于邻域噪声评价法的图像去噪算法

常用的经典脉冲噪声滤波方法在去除图像脉冲噪声的过程中,常常造成图像细节信息的丢失,导致图像模糊不清.为了克服这一缺陷,提出了一种新的基于局部相似度分析和邻域噪声评价的图像去噪算法.该算法通过分析图像中各像素点的局部相似度来确定图像的轮廓和噪声,再通过邻域脉冲噪声评价法检测出脉冲噪声点,使图像处理仅处理噪声点而保持轮廓像素点不变,更有效地改善了噪声检测精度,并保护了图像的细节特征.实验结果表明,这种新算法较其他经典滤波器具有更有效的图像去噪和细节信息保护性能,具有一定的应用价值.

An Image Denoising Algorithm Based on Neighborhood Noise Evaluation

The loss of information on image details was often found in image denoising process if using the conventionally typical method of impulse noise filtering,which resulted in blurred images.Based on local similarity analysis and neighborhood noise evaluation,a new image denoising algorithm is proposed to analyze the local similarities between all pixels in an image so as to determine the outline and noise of an image.Then,the noises are detected through neighborhood impulse noise evaluation so as to enable the algorithm to just process noise pixels with the pixels of image outlines kept unchanged.In this way,the accuracy of noise detection can be improved more efficiently with image details well preserved.Experimental results showed that the new algorithm outperforms other prior-art methods in suppressing impulse noise and detail preservation,thus offering a new filter applicable to image processing.