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Image Enhancement -Part 1 - Image Enhancement Part 1 1...

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    1 Image Enhancement Part 1
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2 Image Enhancement Used to emphasize and sharpen image  features for display and analysis. enhance otherwise hidden information Filter important image features Discard unimportant image features Enhancement method are application  specific.
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3
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4 Imput Image I(r,c) Frequency Domain Output Image E(r,c) Spatial Domain Application-Specific Feedback
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    5 Image Enhancement In Spatial Domain Refers to the image plane  (the ‘natural’ image) Direct image manipulation
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Remember ? A 2D grayvalue - image is a 2D -> 1D function, v = f(x,y)
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Remember ? As we have a function, we can apply operators to this function, e.g. T(f(x,y)) = f(x,y) / 2 Operator Image (= function !)
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Remember ? As we have a function, we can apply operators to this function, e.g. T(f(x,y)) = f(x,y) / 2 Operator Image (= function !)
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Remember ? T transforms the given image f(x,y) into another image g(x,y) f(x,y) g(x,y)
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Spatial Domain The operator T can be defined over The set of pixels (x,y) of the image The set of ‘neighborhoods’ N(x,y) of each pixel A set of images f1,f2,f3,…
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Operation on the set of image-pixels 6 8 2 0 12 200 20 10 3 4 1 0 6 100 10 5 Spatial Domain (Operator: Div. by 2)
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Operation on the set of ‘neighborhoods’ N(x,y) of each pixel 6 8 2 0 12 200 20 10 226 Spatial Domain 6 8 12 200 (Operator: sum)
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Operation on a set of images f1,f2,… 6 8 2 0 12 200 20 10 Spatial Domain 5 5 1 0 2 20 3 4 11 13 3 0 14 220 23 14 (Operator: sum)
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Operation on the set of image-pixels Remark: these operations can also be seen as operations on the neighborhood of a pixel (x,y), by defining the neighborhood as the pixel itself. The easiest case of operators g(x,y) = T(f(x,y)) depends only on the value of f at (x,y) T is called a gray-level or intensity transformation function Spatial Domain
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Basic Gray Level Transformations Image Negatives Log Transformations Power Law Transformations Piecewise-Linear Transformation Functions For the following slides L denotes the max. possible gray value of the image, i.e. f(x,y) [0,L] Transformations
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Image Negatives: T(f)= L-f Transformations Input gray level Output gray level T(f)=L-f
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Log Transformations: T(f) = c * log (1+ f) Transformations Log/ Expand gray levels InvLog/ Compress gray levels
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Log Transformations Transformations
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Power Law Transformations T(f) = c*f γ Transformations
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