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Restoration of Digitized Old Paintings & Manuscripts Varun Varshney (200701085) Siddharth...

Date post:18-Dec-2015
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  • Slide 1
  • Restoration of Digitized Old Paintings & Manuscripts Varun Varshney (200701085) Siddharth Kherada (200702048) Restoration of Paintings : Crack Removal Crack detection, classification and crack filling. Restoration of Manuscripts : Foxing Removal & Page Enhancement Foxing detection, removal and color correction Research Paper: Digital image processing techniques for the detection and removal of cracks in digitized paintings By: Ioannis Giakoumis, Nikos Nikolaidis, Ioannis Pitas Research paper:-Digital Automated restoration of manuscripts and antique printed books. By:-Giovanni Ramponi, Filippo Stanco, William Dello Russo, Simonetta Pelusi, Paolo Mauro.
  • Slide 2
  • Restoration of Old Paintings :Crack Removal Removal of Cracks Need: Old Paintings are being digitized. Problem: Removal of cracks without corrupting the brush strokes. Step 1:Crack Detection Using Non linear Morphological top hat filter Result: Binarized image containing cracks +dark brush strokes Step2:Crack Classification Classification of cracks and dark brush strokes using gaussian classifier Result: Cracks obtained Step 3: Crack Filling Crack filling based on order statistics filters(MTM filter) Result: Cracks interpolated or filled ! Process Diagram:--
  • Slide 3
  • Crack Detection & Crack Classification: Crack detection(top hat transform) Crack classification( gaussian classifier) Crack detection Crack classification : Gaussian Before classification : Crack :3662, brush:816, undecided:3607 After classification : Crack :6739, brush:1346, undecided:0 Crack classification : Gaussian Before classification : Crack :3662, brush:816, undecided:3607 After classification : Crack :6739, brush:1346, undecided:0
  • Slide 4
  • Results (MTM filter) Brush strokes are still intact whereas cracks are reduced
  • Slide 5
  • Results with other images of Byzantine era
  • Slide 6
  • Slide 7
  • Restoration of Old Manuscripts Foxing Detection & Removal NEED : Libraries and other cultural institutions are digitizing increasing quantities of manuscript and printed material, both ancient and modern. PROBLEM : FOXING : describes the red-brown spots (color of a fox) that may form on the surface of the paper of antique books. It is often triggered by high humidity and temperature extremes in places where books and documents are stored. Page Enhancement NEED : Important step after Foxing Removal for reducing the yellowing of the paper. PROBLEM : FOXING : removal of yellowing due to cellulose oxidation catalyzed by metals.
  • Slide 8
  • Process Diagram:Foxing Removal 1)Foxing Detection: Operate on Chrominance Red(Cb). Threshold for Binary Image 1)Foxing Detection: Operate on Chrominance Red(Cb). Threshold for Binary Image 4)Color Filtering This is done by using median of non foxed Pixels. 4)Color Filtering This is done by using median of non foxed Pixels. 2)Detail Image Extraction: Rational Filter To get Ylp. Y hp =Y-Y lp +K K determined Using mean & Otsus threshold 2)Detail Image Extraction: Rational Filter To get Ylp. Y hp =Y-Y lp +K K determined Using mean & Otsus threshold 3)Processing Of Fox image & Merging withY hp. Processing using RF Ffox. Merging using Ffox and original image 3)Processing Of Fox image & Merging withY hp. Processing using RF Ffox. Merging using Ffox and original image Original Final DeFoxed
  • Slide 9
  • Foxing Removal :Results 3 3 2 2 1 1 1)Original Image 2)Result in paper 3)Our Result 1)Original Image 2)Result in paper 3)Our Result Page Enhancement (to be done) As the page quality decreases Page Enhancement (to be done) As the page quality decreases
  • Slide 10
  • Foxing Removal :Results on other Images
  • Slide 11
  • Process Diagram:Foxing Removal 1)Tonal Adjustment: Application of Non linear func. with sigmoidal shape on a RF Filtered image (Y r ) Result : (Y r1 ) 1)Tonal Adjustment: Application of Non linear func. with sigmoidal shape on a RF Filtered image (Y r ) Result : (Y r1 ) 4)Color Correction This is done by using median of non foxed Pixels. 4)Color Correction This is done by using median of non foxed Pixels. 2)Detail Preservation & Finish Tonal Adjustment: Pixel by pixel of original and RF filtered image. Result: Y n =Y*Y/Y r 2)Detail Preservation & Finish Tonal Adjustment: Pixel by pixel of original and RF filtered image. Result: Y n =Y*Y/Y r 3)Threshol- ding using Otsus threshold. Result : (Y bw ) 3)Threshol- ding using Otsus threshold. Result : (Y bw ) Final DeFoxed Original(after foxing removal
  • Slide 12
  • Page Enhancement : Results
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