spatial2.slides.printing.2

# spatial2.slides.printing.2 - More Spatial Filtering More...

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More Spatial Filtering More Spatial Filtering CS 450: Introduction to Digital Signal and Image Processing Bryan Morse BYU Computer Science More Spatial Filtering Sharpening Sharpening The goal of sharpening is to enhance differences , so all sharpening kernels involve differences— some positive and some negative weights. I Unsharp masking I 1st-derivative operators I 2nd-derivative operators

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More Spatial Filtering Sharpening Unsharp Masking I Idea: “subtract out the blur” I Can be done in analog (originated in dark rooms) I Procedure: 1. Blur the image 2. Subtract from the original 3. Multiply by some weighting fraction 4. Add back to the original More Spatial Filtering Sharpening Unsharp Masking Mathematically: g = f + α ± f - f ² where I f is the original image I f is the blurred image I g is the sharpened result I α controls how much sharpening is added
More Spatial Filtering Sharpening Unsharp Masking - Alternative Formulation I Unsharp Masking: g = f + α ± f - f ² I Rather than working with fractional weights α , we can write this as g = Af + ± f - f ² I In other words, what really matters is the ratio of the original image to the “unsharp masked” (sharpening) part, not the absolute values used. I

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spatial2.slides.printing.2 - More Spatial Filtering More...

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