数字图像处理英文版.ppt

* 常用的一阶梯度算子 * 处理效果比较 一阶微分算子的效果 （b）原图 （b）Robert算子 （c）Sobel算子 （d）Priwitt算子 * 3.7.1 First-order derivative Image Enhancement: 梯度图像直接输出 　 Enhancement:Gradient 加阀值的梯度输出 T＞0 优点：适当选取T，既可使明显的边缘轮廓得到突出，又不会破坏原灰度变化比较平缓的背景。 * 轮廓灰度规定化输出 背景灰度规定化输出 式中：T是根据需要指定的一个灰度级，它将明显边缘用一固定的灰度级LG来实现。使边界清晰、轮廓突出、背景不破坏。 此法将背景用一个固定灰度级LB来实现，便于研究边缘灰度的变化。 3.7.1 First-order derivative Enhancement:Gradient * 二值图像输出 此法将背景和边缘用二值图像表示， 便于研究边缘所在位置。 一般取LG=255,LB=0。如字符识别等。 3.7.1 First-order derivative Enhancement:Gradient * 梯度锐化实例 I=imread(cameraman.tif) subplot(131), imshow(I) H=fspecial(Sobel) H=H TH=filter2(H, I) subplot(132), imshow(TH, []) H=H TH=filter2(H, I) subplot(133), imshow(TH, []) * 1 ) Laplacian Operator Notation： Implementation： 即： 3.7.2 Second-order derivative * Laplacian Operator * Laplacian算法效果图 * First-order derivatives produce thicker edges Second-order derivatives have a stronger response to fine detail(thin line and isolated points) First-order derivatives have a stronger response to a gray-level step Second-order derivatives produce a double response at step changes Observation on derivatives * Highlights gray-level discontinuities Deemphasizes regions with slowly varying levels Produce images that have grayish edge lines and other discontinuities, all superimposed on a dark,featureless background. if the center coefficient is negative if the center coefficient is positive 3.7.2 Second Derivatives for Enhancement: Laplacian * Sharpening by Laplacian operator * Second Derivatives for Enhancement: Laplacian * Simplification Simplification of Laplacian operators * Original SEM image Laplacian operator Subtraction of the Laplacian from the original Original image Laplacian operator Subtraction of the Laplacian from the original Second Derivatives for Enhancement: Laplacian * Subtract low-pass filtered version from the original Emphasize high frequency information High boosting filter: Unsharp Masking Filtering Unsharp Masking filter: * i