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Adaptive Enhancement Gang Wang Adaptive Adaptive unsharp masking An adaptive image enhancement algorithm.

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Presentation on theme: "Adaptive Enhancement Gang Wang Adaptive Adaptive unsharp masking An adaptive image enhancement algorithm."— Presentation transcript:

1 Adaptive Enhancement Gang Wang Adaptive Adaptive unsharp masking An adaptive image enhancement algorithm

2 Adaptive Unsharp Masking Objective: emphasize the medium-contrast details than large- contrast details Linear unsharp masking

3 Adaptive Method Define a “desired output”, and adjust the weight factor to cause the output to agree as closely as possible with the desired output. Adaptation algorithm to recursively update

4 Quantify the regions Define smooth, medium-contrast, high-contrast regions

5 Local dynamics g() highpass filter to define local dynamics 8

6 Define “desired output”

7 Adaptation algorithm LMS/Newton algorithm(Gauss-Newton) Compute enhanced image

8 Demo original imagelinear UM adaptive UM

9 An adaptive image enhancement algorithm Objective: smooth the uniform areas and sharpen the borders Method (1) determine each pixel in homogenous region or not (Haralick method). (2) according to the position property, assign to each pixel a new digital number.

10 Haralick method

11 Haralick method (cont) F has an F distribution with 2,6 degrees of freedom (considering 3* 3 window,n=9) We are interested in a1=0,a2=0 (homogenous region) 1% significance test if F<10.9, homogenous region,otherwise not 5% significance test if F<5.14, homogenous region,otherwise not

12 Example

13 Results of F-test Use F-test to determine whether the window in a particular position is homogenous or not, if yes, mark 1;if not, mark Results of F-test

14 Step 2: image enhancement procedure Based on the F-test matrix, the image enhancement procedure is accomplished by assigning new digital number to each pixel, through this, to sharpen the border and smooth the uniform area. Compute RV(reference value): If F_test(x,y)=1 RV is the average within the window. If F_test(x,y)=2 RV is taken as the highest or the lowest digital number within the window, the one closest to the digital number of the central pixel. Compute new digital number: DN(new)=WF*RV+(1-WF)*DN(old).

15 Demo original image enhanced image (5%) (one iteration) enhanced image (1%) (four iteration) enhanced image (1%) (one iteration) enhanced image (1%) 3 level (one iteration)


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