论文标题
高斯调制的双曲线切线高通滤波器在嘈杂的图像中用于边缘检测
Gaussian modulated hyperbolic tangent high pass filter for edge detection in noisy images
论文作者
论文摘要
在本文中，不可分割的（NS），稳健到噪声，二维（2D）各向同性高斯调制的双曲线切线（GMHPT）高通（HP）滤光片的设计旨在过滤嘈杂图像中存在的高频组件。用于图像分析的常规HP滤波器的主要缺点是噪声影响（对噪声的敏感性更高），这会导致虚假响应和由于其可分离性能而导致的方向选择性有限。设计的过滤器采用GMHBT频率响应，以确保在最小平方的感觉中提高噪声性能。 2D NS滤波器具有更好的定向选择性，GMHBT轮廓提供了更少的噪声灵敏度以及正规化，最小成方误差设计。通过恢复图像的高频组件来改善高通滤波，以峰信号与噪声比（PSNR）（PSNR）进行测量，并与高斯（Log）（log）滤波器的二维，不可分割的各向同性Laplacian进行比较。设计的过滤器可在PSNR中改善3DB。
In this paper, a non-separable (NS), robust to noise, Two Dimensional (2D) isotropic Gaussian Modulated Hyperbolic Tangent (GMHPT) High Pass (HP) filter is designed to filter the high-frequency components present in a noisy image. The major drawbacks of conventional HP filters used for image analysis are noise influences (more sensitivity to noise) which lead to spurious responses and limited directional selectivity due to their separable property. The designed filter employs GMHBT frequency response to ensure improved noise performance in the least square sense. The 2D NS filter has better directional selectivity and GMHBT profile offers less noise sensitivity along with regularization by least square error design. Improvement of high pass filtering in noisy images, by means of restoring the high-frequency components of the image is measured with Peak Signal to Noise Ratio (PSNR) and compared with two dimensional, non-separable isotropic Laplacian of Gaussian (LoG) filter. The designed filter gives 3dB improvement in the PSNR.
