摘要
锐化是图像增强巾一项关键性的技术,但如果图像中包含噪声,噪声也会囚为锐化而放大,最终导致信噪比的降低。探索了一种算法既可以对图像进行锐化滤波,又不降低图像的信噪比。采用模式识别的相关理论,基于隶属度和概率松弛技术对红外图像中由真实边缘和由各种噪声引起的亮度数值变化进行区分,对不同区域采用不同的锐化处理。该算法不同于传统图像锐化算法只基于局部对比度的缺点,在图像锐化过程中考虑图像边缘和噪声的空间分布的差异,改善了传统边缘增强算法对噪声放大的缺点。实验数据表明,该锐化方法未引起信噪比的降低,具有良好的前景和实用价值。
Sharpening is a key technology of infrared image enhancement, but the image＇s noise will be enhanced because of the sharpening, and the signal-to-noise ratio will decrease. The algorithm is researched to sharpen the image without increasing the noise. The distribution difference between random noise and the real edge is analyzed, and the definitions of the membership and the probability relaxation are advanced to distinguish the illumination changes caused by real edges or noise in the infrared image. An algorithm based on the two methods are introduced. Different from the tranditional algorithms, this algorithm considers the spatial difference between the real edge and the noise. So this algorithm not only enhances the details, but also reduces the enhancement of random noise. As it is shown in experiments, the edge of the image is sharpened and the noise is suppressed. This algorithm has practicality and potential application value in the field of infrared images contrast enhancement.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2009年第7期1807-1811,共5页
Acta Optica Sinica
关键词
图像处理
红外图像
锐化
隶属度
概率松弛技术
image processing
infrared image
sharpening
membership
probability relaxation
