Afdeling Toegepaste Wiskunde/ Division of Applied Mathematics Colour image processing(6.4 and 6.5) SLIDE 1/10

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1 Colour image processing(6.4 and 6.5) SLIDE 1/ Basics of full-colour image processing Two categories: (1) Process each component image individually and form composite processed colour image from the individually processed components (2) Work with colour pixels directly; colour pixels really are vectors: c(x,y)= c R(x,y) c G (x,y) c B (x,y) = R(x,y) G(x, y) B(x, y) Note: the results of individual colour component processing are not always equivalent to direct processing in colour vector space Processing is equivalent if: (1) the process is applicable to both scalars and vectors; (2) theoperationoneachcomponentofavector is independent of the other components

2 Colour image processing(6.4 and 6.5) SLIDE 2/10 Illustration: Neighbourhood averaging Result for per-colour-component and vector-based processing is equivalent. Why? 6.5 Colour Transformations (Consider single model) Formulation Modelcolourtransformationswithg(x,y)=T[f(x,y)] Pixel values here are triplets or quartets Analogous to section 3.2(gray-level), we now consider s i =T i (r 1,r 2,...,r n ), i=1,2,...,n

3 Colour image processing(6.4 and 6.5) SLIDE 3/10 Some operations are better suited to specific models, but cost of converting between representations has to be considered as well! Example follows...

4 Colour image processing(6.4 and 6.5) SLIDE 4/10 Supposethatwewishtomodifytheintensityoftheimageonpage4using g(x,y)=kf(x,y),where0<k<1 HSIcolourspace: s 1 =r 1,s 2 =r 2,s 3 =kr 3 RGBcolourspace: s i =kr i, i=1,2,3 CMYcolourspace: s i =kr i +(1 k), i=1,2,3 Although the HSI transformation involves the fewest number of operations, thecomputationsrequiredtoconvertanrgborcmy(k)imagetothehsi space more than offsets the advantages of the simpler transformation

5 Colour image processing(6.4 and 6.5) SLIDE 5/ Colour complements Complements: hues opposite one another on colour circle Useful for enhancing detail embedded in dark regions

6 Colour image processing(6.4 and 6.5) SLIDE 6/ Colour slicing Highlight a range of colours to separate objects from their surroundings. Thebasicideaiseitherto (1) display the colours of interest or (2) usetheregionasamaskforfurtherprocessing Methods for slicing a colour image: (1) Colours of interest inclosed by cube (hypercube) of width W and centeredat(a 1,a 2,...,a n ) ) (anyj [1,n]) ( 0.5 if rj a j > s i ={ W 2,i [1,n] r i otherwise (2) Colours of interest inclosed by sphere (hypersphere) of radius R 0 andcenteredat(a 1,a 2,...,a n ) n 0.5 if (r j a j ) 2 >R0 2 s i =,i [1,n] j=1 r i otherwise

7 Colour image processing(6.4 and 6.5) SLIDE 7/10 Example 6.8: An illustration of colour slicing Tone and colour corrections Wedonotdiscussthetheoreticaspectsonpage455andimmediatelyproceed to Examples 6.9(tonal transformations) and 6.10(colour balancing)...

8 Colour image processing(6.4 and 6.5) SLIDE 8/10 Example 6.9: Tonal corrections

9 Colour image processing(6.4 and 6.5) SLIDE 9/10 Example 6.10: Colour balancing(cmyk images)

10 Colour image processing(6.4 and 6.5) SLIDE 10/ Histogram processing Generally unwise to equalize colour components independently: results in erroneous colour Rather spread colour intensities uniformly and leave the hues unchanged: HSI colour space well-suited for this approach Example 6.11: Histogram equalization(hsi space) (a) Original image(b)intensity transformation& histograms(c) image after histogram equalization(d) saturation after histogram equalization