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Image processing

Image processing. Enhancement and restoration. Image Histogram. is a chart that shows the distribution of intensities in an indexed or intensity image.

leandra-buchanan
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Image processing

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  1. Image processing Enhancement and restoration

  2. Image Histogram • is a chart that shows the distribution of intensities in an indexed or intensity image. • The image histogram function imhistcreates this plot by making n equally spaced bins, each representing a range of data values. It then calculates the number of pixels within each range.

  3. Histogram of stars and Pout I = imread('ngc4024l.tif'); figure, imhist(I,255) I = imread(‘pout.tif'); figure, imhist(I,255);

  4. Histogram equalization • Let H(p) be the input histogram and that the input gray-scale is [po, pk] • The intention is to find a monotonic pixel brightness q=T(p) such that the output histogram G(q) is uniform over the whole output brightness scale [qo, qk] • The histogram is a discrete probability density function. • The monotonic property of the transform T implies

  5. Histogram equalization • The aim is to produce an image with equally distributed brightness levels over the whole brightness scale.

  6. Histogram equalization • Assume image of MxN pixels • The equalized histogram corresponding to the uniform probability density function f whose function value is a constant: • The equalized histogram can be obtained precisely only for the ``idealized'' continuous probability density, in which case the sum equation becomes

  7. Histogram equalization • The desired pixel brightness transformation T can then be derived as • The integral in the above equation is called the cumulative histogram, which is approximated by a sum for digital images.

  8. Histogram equalization • So the resulting histogram is not equalized ideally. • The discrete approximation of the continuous pixel brightness transformation form the above equation is

  9. Histogram equalization I = imread('pout.tif'); J = histeq(I); imshow(I) figure, imshow(J)

  10. filter2 and imfilter for image filtering. • filter2(h,A) filters the data in A with the two-dimensional filter in the matrix h. • A filter is to be specified by matrix h with fspecial. h = fspecial(type) h = fspecial(type,parameters) • Imfilter is for multidimensional image filtering B = imfilter(A,H) B = imfilter(A,H,option1,option2,...) Filter Options • 'corr‘: imfilter performs multidimensional filtering using correlation, which is the same way that filter2 performs filtering. When no correlation or convolution option is specified, imfilter uses correlation. • 'conv‘ : imfilter performs multidimensional filtering using convolution.

  11. fspecial h = fspecial(type) h = fspecial(type,parameters) • h = fspecial(type) creates a two-dimensional filter, h, of the specified type. • fspecial returns h as a correlation kernel, which is the appropriate form to use with imfilter. type is a string having one of these values: • 'gaussian' for a Gaussian lowpass filter • 'sobel' for a Sobel horizontal edge-emphasizing filter • 'prewitt' for a Prewitt horizontal edge-emphasizing filter • 'laplacian' for a filter approximating the two-dimensional Laplacian operator • 'log' for a Laplacian of Gaussian filter • 'average' for an averaging filter • 'unsharp' for an unsharp contrast enhancement filter

  12. Average and Median filter • For example, for averaging filter for the window of size of 3x3, the filtered image K is obtained as K= filter2(fspecial('average',3),I)/255; • Median filter L = medfilt2(I,[3 3]);

  13. Noise removal IMNOISE function add a noise to image. • J = IMNOISE(I,TYPE,...) Add noise of a given TYPE to the intensity image I. TYPE is a string that can have one of these values: 'gaussian' Gaussian white noise with constant mean and variance 'localvar' Zero-mean Gaussian white noise with an intensity-dependent variance 'poisson' Poisson noise 'salt & pepper' "On and Off" pixels 'speckle' Multiplicative noise

  14. Add noise I = imread(‘pout.tif'); J = imnoise(I,'gaussian'); figure, imshow(J);

  15. Filtering a Region • specify a region using roipoly function. If you call roipoly with no input arguments, the cursor changes to a cross hair when it is over the image displayed in the current axes. You can then specify the vertices of the polygon by clicking on points in the image with the mouse. Doubleclick to finish. I = imread('pout.tif'); imshow(I) BW = roipoly; imshow(BW)

  16. roifilt2 • use the roifilt2 function to process a region of interest. • When you call roifilt2, you specify an intensity image, a binary mask, and a filter. • roifilt2 filters the input image and returns an image that consists of filtered values for pixels where the binary mask contains 1's, and unfiltered values for pixels where the binary mask contains 0's. • This type of operation is called masked filtering.

  17. Region of interest filtering h = fspecial('unsharp'); I2 = roifilt2(h,I,BW); figure, imshow(I2)

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