# ch03d - Ch3 Spatial Filtering 2 Overview Spatial Highpass...

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Ch3 – Spatial Filtering 2 • Overview • Spatial Highpass Filter • Unsharp Masking • Variance Based Enhancement • Wallis Operator • Derivative Filters • Laplacian Filters • Conclusion

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Overview • In this section, we focus on techniques that sharpen images to enhance small details • Image sharpening can be thought of as the ‘dual’ of image smoothing • Consider an image as sum of two parts: original(x,y) = smooth(x,y) + sharp(x,y) smooth(x,y) = original(x,y) – sharp(x,y) sharp(x,y) = original(x,y) – smooth(x,y)
Spatial Highpass Filter • A spatial highpass filter does the opposite of neighborhood averaging • Each (x,y) pixel in the image is replaced by the difference between the original pixel value and the average of pixels in an NxN neighborhood centered at (x,y) • This can be implemented using an NxN convolution mask that combines averaging and subtraction

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Spatial Highpass Filter Original Average Highpass
Spatial Highpass Filter Original Binomial Highpass

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• Unsharp masking is a very old photographic technique • Here we subtract a blurred image from original to get unsharp mask image • Then we add a multiple of unsharp mask to original to get sharpened image
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## This note was uploaded on 12/01/2011 for the course CSCE 5013 taught by Professor Staff during the Fall '08 term at Arkansas.

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ch03d - Ch3 Spatial Filtering 2 Overview Spatial Highpass...

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