PROJECT TITLE :

Sparsity-based inverse halftoning

ABSTRACT :

Proposed is the sparsity-based mostly inverse halftoning technique, in that a sparse prior that reflects the natural gradient statistics is employed with the sparse illustration of the lowpass-filtered halftoned patches paired with the corresponding continuous patches to resolve a regularised deconvolution. The experimental results show that the proposed method that was based mostly on the sparsity, i.e. the combination of the sparse previous and therefore the sparse representation, will reconstruct an unknown continuous image with less noise and fine details from an input halftoned image.


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