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Image enhancement techniques

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Image enhancement techniques

  1. 1. 1 IMAGE ENHANCEMENT TECHNIQUES SUBMITTED BYSUBMITTED BY SAUMEN BARUASAUMEN BARUA ROLL :361ROLL :361 COMPUTER SCIENCECOMPUTER SCIENCE ADVISORADVISOR MR. ANISUR RAHMANMR. ANISUR RAHMAN
  2. 2. 2 INTRODUCTION  Image enhancement widely used inImage enhancement widely used in computer graphics.computer graphics.  It is the sub areas of image processing.It is the sub areas of image processing.  The principle objectives of imageThe principle objectives of image enhancement techniques is to process anenhancement techniques is to process an image so that the result is more suitableimage so that the result is more suitable than the original image for a specificthan the original image for a specific applicationapplication ..
  3. 3. 3 METHODS FOR IMAGE ENHANCEMENT  Image enhancement techniques can beImage enhancement techniques can be divided into two broad categories:divided into two broad categories:  1.Spatial domain methods .1.Spatial domain methods .  2 Frequency domain methods.2 Frequency domain methods.
  4. 4. 4 SPATIAL DOMAIN METHODS  The term spatial domain refers to the aggregate ofThe term spatial domain refers to the aggregate of pixels composing an image. Spatial domainpixels composing an image. Spatial domain methods are procedures that operate directly onmethods are procedures that operate directly on these pixels. Spatial Domain processes will bethese pixels. Spatial Domain processes will be denoted by the expression ,denoted by the expression , g(x,y)= T[f(x,y)]g(x,y)= T[f(x,y)]
  5. 5. 5 POINT PROCESSING  It is the process of contrast enhancement.It is the process of contrast enhancement.  It is the process to produced an image of higherIt is the process to produced an image of higher contrast than the original by darkening a particularcontrast than the original by darkening a particular level.level.  Enhancement at any point in an image dependsEnhancement at any point in an image depends only on the gray level at that point techniques inonly on the gray level at that point techniques in this category ore often referred to as pointthis category ore often referred to as point processing.processing.
  6. 6. 6 Median and Max/Min filtering  Median filtering is a powerful smoothingMedian filtering is a powerful smoothing technique that does not blur the edgestechnique that does not blur the edges significantlysignificantly ..  Max/min filtering is used where the max orMax/min filtering is used where the max or min value of the neighbourhood gray levelsmin value of the neighbourhood gray levels replaces the candidate pelreplaces the candidate pel ..  Shrinking and expansion are usefulShrinking and expansion are useful operations especially in two tone images.operations especially in two tone images.
  7. 7. 7 IMAGE SUBTRACTION  The difference between two images f(x,y) andThe difference between two images f(x,y) and h(x,y) are expressed as,h(x,y) are expressed as, G(x,y)= f(x,y) – h(x,y)G(x,y)= f(x,y) – h(x,y) Is obtained by computing the difference betweenIs obtained by computing the difference between all pairs of corresponding pixels from f and h. Theall pairs of corresponding pixels from f and h. The key usefulness of subtraction is the enhancementkey usefulness of subtraction is the enhancement of difference between images.of difference between images. One of the most commercially successful andOne of the most commercially successful and beneficial uses of image subtraction is in the areabeneficial uses of image subtraction is in the area of medical imaging called mask modeof medical imaging called mask mode radiography .radiography .
  8. 8. 8 HISTOGRAM EQUALIZATION  Histogram equalization is one of the mostHistogram equalization is one of the most important parts for any image processingimportant parts for any image processing ..  This technique can be used on a wholeThis technique can be used on a whole image or just on a part of an image.image or just on a part of an image.  Histogram equalization can be used toHistogram equalization can be used to improve the visual appearance of an image.improve the visual appearance of an image.
  9. 9. 9 FREQUENCY DOMAIN METHODS  We compute the Fourier transform of theWe compute the Fourier transform of the image to be enhanced, multiply the result byimage to be enhanced, multiply the result by a filter (rather than convolve in the spatiala filter (rather than convolve in the spatial domain), and take the inverse transform todomain), and take the inverse transform to produce the enhanced image.produce the enhanced image.
  10. 10. 10 IMAGE SMOOTHING  The aim of image smoothing is to diminishThe aim of image smoothing is to diminish the effects of camera noise, spurious pixelthe effects of camera noise, spurious pixel values, missing pixel values etc.values, missing pixel values etc. Two methods used for image smoothing.Two methods used for image smoothing. neighborhood averaging and edge-neighborhood averaging and edge- preserving smoothing.preserving smoothing.
  11. 11. 11 Neighbourhood Averaging  Each point in the smoothed image,F(X,Y) isEach point in the smoothed image,F(X,Y) is obtained from the average pixel value in aobtained from the average pixel value in a neighbourhood of (neighbourhood of (xx,,yy) in the input image.) in the input image.  For example, if we use a 3*3For example, if we use a 3*3 neighbourhood around each pixel we wouldneighbourhood around each pixel we would use the mask .Each pixel value is multiplieduse the mask .Each pixel value is multiplied by 1/9, summed, and then the result placedby 1/9, summed, and then the result placed in the output imagein the output image
  12. 12. 12 Edge preserving smoothing  An alternative approach is to useAn alternative approach is to use median filteringmedian filtering instead of neighborhood averaginginstead of neighborhood averaging..  Here we set the grey level to be the median of theHere we set the grey level to be the median of the pixel values in the neighborhood of that pixel.pixel values in the neighborhood of that pixel.  The outcome of median filtering is that pixels withThe outcome of median filtering is that pixels with outlying values are forced to become more likeoutlying values are forced to become more like their neighbors, but at the same time edges aretheir neighbors, but at the same time edges are preserved ,so this also known aspreserved ,so this also known as edge preservingedge preserving smoothing.smoothing.
  13. 13. 13 Image sharpening  The main aim in image sharpening is toThe main aim in image sharpening is to highlight fine detail in the image, or tohighlight fine detail in the image, or to enhance detail that has been blurredenhance detail that has been blurred
  14. 14. 14 Conclusion  The aim of image enhancement is to improve theThe aim of image enhancement is to improve the information in images for human viewers, or toinformation in images for human viewers, or to provide `better' input for other automated imageprovide `better' input for other automated image processing techniquesprocessing techniques  There is no general theory for determining what isThere is no general theory for determining what is `good' image enhancement when it comes to`good' image enhancement when it comes to human perception. If it looks good, it is good!human perception. If it looks good, it is good!
  15. 15. 15 THANK YOUTHANK YOU

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