adaptive unsharp masking

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ADAPTIVE UNSHARP MASKING Digital Image Processing

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A presentation about the adaptive unsharp masking applied in image processing. Unsharp masking leads to edge enhancement in images..

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Page 1: Adaptive unsharp masking

ADAPTIVE UNSHARP MASKING

Digital Image Processing

Page 2: Adaptive unsharp masking

Contents

• What is Unsharp Masking..?

• Concept of Operation

• Drawbacks

• Adaption Algorithm

• Advantages of Unsharp Masking

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Unsharp Masking

• Unsharp masking (USM) is an image manipulation technique.

• An unsharp mask cannot create additional detail, but it can greatly enhance the appearance of detail by increasing small-scale acutance (the edge contrast of an image).

• Normally an "unsharp mask" is used to sharpen an image which can help us to emphasize texture and detail of the image.

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Concept of Operation

• Normally in Unsharp Masking, a highpass filtered, scaled version of an image is added to the image itself.

• This will improve the visual appearance of an image significantly by emphasizing its high frequency contents to enhance the edge and detail information in it.

• Generally an unsharp mask is a filter that amplifies high-frequency components.

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• The enhanced image y(n,m) is obtained from the input image x(n,m) as

y(n,m) = x(n,m) + ƛz(n,m)

• where z(n,m) is the correction signal computed as the output of a linear high pass filter.

• ƛ is the positive scaling factor that controls the level of

contrast enhancement achieved at the output.

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hx0 -1 0

0 2 0

0 -1 0

hy0 0 0

-1 2 -1

0 0 0

z(n,m) = [zx(n,m), zy(n,m)]T

y(n,m) = x(n,m) + ƛz(n,m)

Zx=conv(x,hx) Zy=conv(x,hy)

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Zx Zy

Original Image(x) Output Image(y)

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Drawbacks

• Even though this method is simple and works well in many applications, it suffers from two main drawbacks.

i) The presence of the linear high pass filter makes the systemextremely sensitive to noise. This results in perceivable andundesirable distortions in the images.

ii) It enhances high-contrast areas much more than areas thatdo not exhibit high image dynamics. Consequently, someunpleasant overshoot artifacts may appear in the outputimage.

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Adaptive Unsharp Masking

• In this approach, we introduce a variation of the basicUnsharp Masking scheme that contains an adaptive filter inthe correction path.

• The objective of the adaptive filter is to emphasize themedium-contrast details in the input image more than large-contrast details such as abrupt edges so as to avoid overshooteffects in the output image.

• The adaptive filter does not perform a sharpening operationin smooth areas, and therefore the overall system is morerobust to the presence of noise in the input images thantraditional approaches.

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Implementation diagram

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ƛ (n,m) = [ƛx(n,m), ƛy(n,m)]T

y(n,m) =x(n,m) + ƛx(n,m)zx(n,m)+ ƛy(n,m)zy(n, m)

y(n,m) = x(n,m) + ƛz(n,m)

Here we go with the approach as below :

where ƛ is the positive scaling factor that controls the level of

contrast enhancement achieved at the output.

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Objective of this approach

• In the previous equation, ƛx(n,m) and ƛy (n,m) are thescaling factors for the two components of the correctionsignal at the (n,m)th pixel.

• Our objective is to recursively update these parametersusing an adaptation algorithm so as to produce an outputimage whose local dynamics are increased in the detailareas and left unchanged in the uniform areas.

• i,.e., little or no enhancement is applied in smooth areas ofthe image, maximum enhancement is applied in mediumcontrast areas, and large contrast areas are only moderatelyenhanced.

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Local dynamics

• The local variance computed over a 3 × 3 pixel block is given by :

• To determine this classification, we first measure the localdynamics of the image.

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g-1 -1 -1

-1 8 -1

-1 -1 -1

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Adaption Algorithm

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• This process is iteratively repeated until we acquire the ‘error(e)’ in the desired range.

• When the desired range of error is achieved, we stop the procedure and display the image y which is defined as

y(n,m) =x(n,m) + ƛx(n,m)zx(n,m)+ ƛy(n,m)zy(n, m)

Original Unsharped Image

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Advantages of Unsharp Masking

• Enhanced apparent sharpness through “edge effects”.

• An important advantage of unsharp masking is that it increases the sharpness of the prints.

• Reduction of contrast on negatives of excessive contrast.

• Improved print shadow detail.

• Well known benefits in the printing and graphics arts industries.

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References:

• http://www.ieee.org

• Andrea Polesel, Giovanni Ramponi, and V. John Mathews

“Image Enhancement via Adaptive Unsharp Masking”, IEEE

TRANSACTIONS ON IMAGE PROCESSING

• Google Images

• MATLAB images

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THANK YOU