Adaptive unsharp masking
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Unsharp masking (USM) is an image processing technique that enhances detail by increasing edge contrast through a high-pass filter. The adaptive unsharp masking approach aims to minimize overshoot artifacts by emphasizing medium-contrast details while being robust against noise in smooth areas. This method uses an iterative adaptation algorithm to optimize scaling factors for correction signals, improving local dynamics in images.
![hx hy
0 -1 0 0 0 0
0 2 0 -1 2 -1
0 -1 0 0 0 0
Zx=conv(x,hx) Zy=conv(x,hy)
z(n,m) = [zx(n,m), zy(n,m)]T
y(n,m) = x(n,m) + ƛz(n,m)](https://image.slidesharecdn.com/adaptiveunsharpmasking-130303114126-phpapp01/85/Adaptive-unsharp-masking-6-320.jpg)
![Here we go with the approach as below :
y(n,m) = x(n,m) + ƛz(n,m)
where ƛ is the positive scaling factor that controls the level of
contrast enhancement achieved at the output.
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)](https://image.slidesharecdn.com/adaptiveunsharpmasking-130303114126-phpapp01/85/Adaptive-unsharp-masking-11-320.jpg)
Adaptive unsharp masking
- 1. ADAPTIVE UNSHARP MASKING Digital Image Processing
- 2. Contents • What is Unsharp Masking..? • Concept of Operation • Drawbacks • Adaption Algorithm • Advantages of Unsharp Masking
- 3. 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.
- 4. 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.
- 5. • 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.
- 6. hx hy 0 -1 0 0 0 0 0 2 0 -1 2 -1 0 -1 0 0 0 0 Zx=conv(x,hx) Zy=conv(x,hy) z(n,m) = [zx(n,m), zy(n,m)]T y(n,m) = x(n,m) + ƛz(n,m)
- 7. Zx Zy Original Image(x) Output Image(y)
- 8. 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 system extremely sensitive to noise. This results in perceivable and undesirable distortions in the images. ii) It enhances high-contrast areas much more than areas that do not exhibit high image dynamics. Consequently, some unpleasant overshoot artifacts may appear in the output image.
- 9. Adaptive Unsharp Masking • In this approach, we introduce a variation of the basic Unsharp Masking scheme that contains an adaptive filter in the correction path. • The objective of the adaptive filter is to emphasize the medium-contrast details in the input image more than large- contrast details such as abrupt edges so as to avoid overshoot effects in the output image. • The adaptive filter does not perform a sharpening operation in smooth areas, and therefore the overall system is more robust to the presence of noise in the input images than traditional approaches.
- 11. Here we go with the approach as below : y(n,m) = x(n,m) + ƛz(n,m) where ƛ is the positive scaling factor that controls the level of contrast enhancement achieved at the output. 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)
- 12. Objective of this approach • In the previous equation, ƛx(n,m) and ƛy (n,m) are the scaling factors for the two components of the correction signal at the (n,m)th pixel. • Our objective is to recursively update these parameters using an adaptation algorithm so as to produce an output image whose local dynamics are increased in the detail areas and left unchanged in the uniform areas. • i,.e., little or no enhancement is applied in smooth areas of the image, maximum enhancement is applied in medium contrast areas, and large contrast areas are only moderately enhanced.
- 13. Local dynamics • To determine this classification, we first measure the local dynamics of the image. • The local variance computed over a 3 × 3 pixel block is given by :
- 14. • g -1 -1 -1 -1 8 -1 -1 -1 -1
- 15. •
- 17. • 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
- 18. 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.
- 19. 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
- 20. THANK YOU