IEEE Trans Image Process - Local learned dictionaries optimized to edge orientation for inverse halftoning.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ imag(2830) propos(1344) filter(1198) }
{ featur(1941) imag(1645) propos(1176) }
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Resumo

A method is proposed for fully restoring local image structures of an unknown continuous-tone patch from an input halftoned patch with homogenously distributed dot patterns, based on a locally learned dictionary pair via feature clustering. First, many training sets consisting of paired halftone and continuous-tone patches are collected, and then histogram-of- oriented-gradient (HOG) feature vectors that describe the edge orientations are calculated from every continuous-tone patch, to group the training sets. Next, a dictionary learning algorithm is separately conducted on the categorized training sets, to obtain the halftone and continuous-tone dictionary pairs, optimized to edge-oriented patch representation. Finally, an adaptively smoothing filter is applied to the input halftone patch, to predict the HOG feature vector of an unknown continuous-tone patch, and to select one of the previously learned dictionary pairs, based on the Euclidean distance between the HOG mean feature vectors of the grouped training sets and the predicted HOG vector. In addition to using the local dictionary pairs, a patch fusion technique is used to reduce some artifacts, such as color noise and overemphasized edges on smooth regions. Experimental results show that the use of the paired dictionary selected by the local edge orientation and patch fusion technique not only reduced the artifacts in smooth regions, but also provided well expressed fine details and outlines, especially in the areas of textures, lines, and regular patterns.

Resumo Limpo

method propos fulli restor local imag structur unknown continuouston patch input halfton patch homogen distribut dot pattern base local learn dictionari pair via featur cluster first mani train set consist pair halfton continuouston patch collect histogramof orientedgradi hog featur vector describ edg orient calcul everi continuouston patch group train set next dictionari learn algorithm separ conduct categor train set obtain halfton continuouston dictionari pair optim edgeori patch represent final adapt smooth filter appli input halfton patch predict hog featur vector unknown continuouston patch select one previous learn dictionari pair base euclidean distanc hog mean featur vector group train set predict hog vector addit use local dictionari pair patch fusion techniqu use reduc artifact color nois overemphas edg smooth region experiment result show use pair dictionari select local edg orient patch fusion techniqu reduc artifact smooth region also provid well express fine detail outlin especi area textur line regular pattern

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