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Title: matlab code for shadow detection and removal
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Title: Digital Image Processing Techniques for the Detection and Removal of Cracks in Digit
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Digital Image Processing Techniques for the Detection a


abstract Many paintings, especially old ones, suffer from breaks in the substrate, the paint, or the varnish. These patterns are usually called cracks or craquelure and can be caused by aging, drying, and mechanical factors. Age cracks can result from nonuniform contraction in the canvas or wood-panel support of the painting, which stresses the layers of the painting. Drying cracks are usually caused by the evaporation of volatile paint components and the cons ....etc

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Title: Digital Image Processing Techniques for the Detection and Removal
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Title: Removal of cracks in digitized paintings
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Title: Image Processing Techniques for the detection and Removal of cracks in Dizitized Pai
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Title: detection and removal of cracks in digitized image
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Posted By: kkkk9999
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Title: Virtual Restoration of Ancient Chinese Paintings using Color Contrast Enhancement
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Posted By: msrama
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Abstract


This work presents a novel algorithm using color contrast enhancement and lacuna texture synthesis is proposed for the virtual restoration of ancient Chinese paintings. Color contrast enhancement based on saturation and de-saturation is performed in the u'v'Y color space, to change the saturation value in the chromaticity diagram, and adaptive histogram equalization then is adopted to adjust the luminance component. Additionally, this work presents a new patching method using the Markov Random Field (MRF) model of texture synthes ....etc

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Title: Detection and Removal of Cracks in Digitized Paintings
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Posted By: brajeshjhaa
Created at: Thursday 17th of August 2017 08:42:52 AM
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Detection and Removal of Cracks in Digitized Paintings
IEE Transactions on Image Processing, Vol. 15, No. 1, January 2006
Language: VB.NET

Abstract:
An integrated methodology for the detection and removal of cracks on digitized paintings is presented in this paper. The cracks are detected by thresholding the output of the morphological top-hat transform. Afterward, the thin dark brush strokes which have been misidentified as cracks are removed using either a median radial basis function neural network on hue and saturation data or ....etc

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