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Vladimir Botchko botchko@lut.fi

Lecture 3. Image Enhancement in Lecture 3. Image Enhancement in Spatial DomainSpatial Domain

Lappeenranta University of Technology (Finland)

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Image Enhancement

Simple intensity transformations Histogram processing (equalization) Image subtraction Image averaging Spatial filtering (smoothing, sharpening) Enhancement. First derivative (gradient)

Example of manual edge enhancement: made by artist.

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Simple intensity transformations

Contrast stretched

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Simple intensity transformations

Intensity level slicing: Original image (top) Thresholded (left) Gray-level slicing (lower right)

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Image Enhancement

Simple intensity transformations Histogram processing Image subtraction Image averaging Spatial filtering (smoothing, sharpening) Enhancement. First derivative (gradient).

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Histogram equalization

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Image Enhancement

Simple intensity transformations Histogram processing Image subtraction Image averaging Spatial filtering (smoothing, sharpening) Enhancement. First derivative (gradient).

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Image subtraction

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Image Enhancement

Simple intensity transformations Histogram processing Image subtraction Image averaging Spatial filtering (smoothing, sharpening) Enhancement. First derivative (gradient).

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Image averaging. Spatial filtering (smoothing)

Original image (upper left) Original + noise (upper right) Smoothed image (lower right) Median smoothing (lower left)

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Order-statistics filter for binary images (when numl=5 then it is

median) Rank filter is used for smoothing after recognition for segmentation. Simple Matlab

code is here:

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Order-statistics filter

First is median filtering result (rank 5 for 3x3 window). Upper part is input image lower part is smoothed image

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Order-statistics filter

Then the rank (rank 3 for 3x3 window). Original image upper, smoothed image is lower

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Sharpening

Second Derivatives. Laplacian

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Image Enhancement

Simple intensity transformations Histogram processing Image subtraction Image averaging Spatial filtering (smoothing, sharpening) Enhancement. First derivative (gradient).

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Median filter and Sobel operator

Gray-level images (from left to right from top to bottom): original texture

synthetic (computer generated) image, the result of recognition corrupted by classification errors, median filtering, edge extraction through Sobel operator, superposition a gray level image and image with edges.