Presentation on theme: "Lecture 6 Sharpening Filters"— Presentation transcript:
1Lecture 6 Sharpening Filters The concept of sharpening filterFirst and second order derivativesLaplace filterUnsharp maskHigh boost filterGradient maskSharpening image with MatLab
2Sharpening Spatial Filters Department of Computer Engineering, CMUSharpening Spatial FiltersTo highlight fine detail in an image or to enhance detail that has been blurred, either in error or as a natural effect of a particular method of image acquisition.Blurring vs. SharpeningBlurring/smooth is done in spatial domain by pixel averaging in a neighbors, it is a process of integrationSharpening is an inverse process, to find the difference by the neighborhood, done by spatial differentiation.
3Department of Computer Engineering, CMU Derivative operatorThe strength of the response of a derivative operator is proportional to the degree of discontinuity of the image at the point at which the operator is applied.Image differentiationenhances edges and other discontinuities (noise)deemphasizes area with slowly varying gray-level values.
4Sharpening edge by First and second order derivatives Department of Computer Engineering, CMUSharpening edge by First and second order derivativesIntensity function f =First derivative f’ =Second-order derivative f’’ =f- f’’ =f’ff’’f-f’’
5First and second order difference of 1D Department of Computer Engineering, CMUFirst and second order difference of 1DThe basic definition of the first-order derivative of a one-dimensional function f(x) is the differenceThe second-order derivative of a one-dimensional function f(x) is the difference
7First and Second-order derivative of 2D Department of Computer Engineering, CMUFirst and Second-order derivative of 2Dwhen we consider an image function of two variables, f(x, y), at which time we will dealing with partial derivatives along the two spatial axes.(linear operator)Laplacian operator (non-linear)Gradient operator
8Discrete form of Laplacian Department of Computer Engineering, CMUDiscrete form of Laplacianfrom
9Department of Computer Engineering, CMU Result Laplacian mask
10Laplacian mask implemented an extension of diagonal neighbors
11Other implementation of Laplacian masks give the same result, but we have to keep in mind that when combining (add / subtract) a Laplacian-filtered image with another image.
12Effect of Laplacian Operator Department of Computer Engineering, CMUEffect of Laplacian Operatoras it is a derivative operator,it highlights gray-level discontinuities in an imageit deemphasizes regions with slowly varying gray levelstends to produce images that havegrayish edge lines and other discontinuities, all superimposed on a dark,featureless background.
13Correct the effect of featureless background easily by adding the original and Laplacian image.be careful with the Laplacian filter usedif the center coefficient of the Laplacian mask is negativeif the center coefficient of the Laplacian mask is positive
14Department of Computer Engineering, CMU Examplea). image of the North pole of the moonb). Laplacian-filtered image withc). Laplacian image scaled for display purposesd). image enhanced by addition with original image1-8
15Mask of Laplacian + addition Department of Computer Engineering, CMUMask of Laplacian + additionto simply the computation, we can create a mask which do both operations, Laplacian Filter and Addition the original image.
16Mask of Laplacian + addition Department of Computer Engineering, CMUMask of Laplacian + addition-15
31Example of Combining Spatial Enhancement Methods want to sharpen the original image and bring out more skeletal detail.problems: narrow dynamic range of gray level and high noise content makes the image difficult to enhance
32Example of Combining Spatial Enhancement Methods solve :Laplacian to highlight fine detailgradient to enhance prominent edgesgray-level transformation to increase the dynamic range of gray levels