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Tree-Structured Method for LUT Inverse Halftoning

Tree-Structured Method for LUT Inverse Halftoning. IEEE Transactions on Image Processing June 2002. Outline. Introduction Halftoning Inverse halftoning Tree-Structured Method Result. Introduction. Halftone A technique to convert a continuous-toe image into a binary image. Introduction.

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Tree-Structured Method for LUT Inverse Halftoning

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  1. Tree-Structured Method for LUT Inverse Halftoning IEEE Transactions on Image Processing June 2002

  2. Outline • Introduction • Halftoning • Inverse halftoning • Tree-Structured Method • Result

  3. Introduction • Halftone • A technique to convert a continuous-toe image into a binary image

  4. Introduction • Halftone • Simple Thresholding • Ordered Dither

  5. Introduciton • Halftone • Error Diffusion

  6. Introduction • Inverse Halftone • Reconstructing a continuous-tone image from its halftoned version

  7. Introduction • Inverse Halftone • Low pass filter • LUT(Look Up Table) • Depending upon the distribution of pixels in the template of the pixel

  8. Tree-Structured LUT(TLUT) • LUT: • Require large memory space • 16-pix template: 2^16 = 64Kbytes • TLUT: • Take advantage of nonexistent patterns and reduce storage • Compressed version of LUT

  9. TLUT • Small template will be used to get a crude inverse halftone • Refined by adaptively adding pixels to the template • Adaptive pixels will be placed in a tree structure

  10. Tree Structure • Each tree node is either split further or a leaf • Nodes are split to refine contone value • Leaf stores a contone value

  11. Initial template … (1,1) (0,1) 32 個

  12. Designing the tree structure • 1. the initial template of size a should be chosen from a neighborhood of the current pixel • Generate initial 2^a tree leaves

  13. Template Selection • Assume that we have P images which have sizes x1*y1, x2*y2, … , xp*yp • Continuous tone images Di(n1, n2) and halftone images Hi(n1, n2), i=1, 2, …, P, (n1, n2) denote the cell location

  14. Designing the tree structure • 2.add leaf using MSE • 2.1 for each leaf t and for each pixel p in NL do the following: assume that the leaf t is split into two nodes with the additional pixel p. calculate the MSE of this tree structure ( ) • 2.2 find the leaf t0 and additional pixel p0 such that is minimum • 2.3 update the tree structure by splitting the tree leaf t0 with the additional pixel p0

  15. Assigning Contone Values to Tree Leaves • Find the tree leaves for each pixel in the training set using the inverse halftoning algorithm • Denote the set of contone values of pixels which have the same tree leaf t ans size at the value of the leaf:

  16. Inverse Halftone with Tree Structure • 1.Find a pattern inside the initial template of size. • 2. if node is a leaf, the contone value is stored in the node and assigned as the inverse halftone value • 3. if node is split into two, the location (i , j) of the additional pixel is stored in the node. Get the halftone value of the pixel which is (i , j) away from the current pixel, if this value is 0(1), then the left(right) node is assigned as current node. Goto step 2.

  17. Results: error diffused images

  18. Results: clustered dot ordered dithered images

  19. Results: dipersed dot ordered dithered images

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