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Image Enhancement by Modifying Gray Scale of Individual Pixels

Image Enhancement by Modifying Gray Scale of Individual Pixels. Goal: to modify an image so that its utilization on a particular application is enhanced. A set of ad hoc tools applicable based on viewer’s specific needs. No general theory on image enhancement exists. Methods: Spatial domain

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Image Enhancement by Modifying Gray Scale of Individual Pixels

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  1. Image Enhancement by Modifying Gray Scale of Individual Pixels

  2. Goal: to modify an image so that its utilization on a particular application is enhanced. A set of ad hoc tools applicable based on viewer’s specific needs. No general theory on image enhancement exists. Methods: Spatial domain Pixel processing Gray level transformation: Data independent histogram processing: Data-dependent Arithmetic ops Spatial filtering Frequency domain filtering Image Enhancement

  3. Gray-level Transforms • Operated on individual pixel’s intensity values: s = T(r). r: original intensity, s: new intensity • Data independent pixel-based enhancement method. • Approaches • Image negatives • Log transform • Power law transform • Piece-wise linear transform

  4. s = T(r) = L-1-r Similar to photo negatives. Suitable for enhancing white or gray details in dark background. Image Negatives

  5. s = T(r) = c log(1+r) expand dark value to enhance details of dark area Log Gray-level Transform

  6. s = T(r) = c sg Gamma correction: to compensate the built-in power law compression due to display characteristics. Power Law Gray-level Transform

  7. Piece-wise Linear Gray-levelTransform • Allow more control on the complexity of T(r). • Contrast stretching • Gray-level slicing • Bit-plane slicing

  8. Contrast Stretching

  9. Gray-level Slicing

  10. Bit-Slicing

  11. Data-dependent pixel-based image enhancement method. Histogram = PDF of image pixels. Assumption: each image pixel is drawn from the same PDF independently (i.i.d.) Several effects of histograms are shown at the right side. Histogram Processing

  12. A gray-level transformation method that forces the transformed gray level to spread over the entire intensity range. Fully automatic, Data dependent, (usually) Contrast enhanced Usually, the discrete-valued histogram equalization algorithm does not yield exact uniform distribution of histogram. In practice, one may prefer “histogram specification” Histogram Equalization

  13. Transform pdf of r to a desired pdf ps(s). A generalization of histogram equalization. Basic idea: Given pr(r) and desired pdf pz(z), find a transform z = T(r), such that P(Z ≤ z) = P(R ≤ r). Histogram Matching

  14. Indirect approach: First equalize the histogram using transform s = T(r). Equalize the desired histogram v = G(z). Set v = s to obtain the composite transform Indirect Method Fig. 3.19

  15. Histogram Matching Example

  16. Histogram for Local Enhancement

  17. Image subtraction Mask mode radiography

  18. Same signal, but different noise realization. Averaging of many such images will enhance SNR. Image Averaging

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