Inverse halftoning with grouping singular value decomposition

The objective of inverse halftoning refers to reconstruct a high quality gray scale image from bi-level halftone image. However, reconstructing continuous-tone images from their halftoned versions is highly underdetermined, making this technique very difficult. In this paper, we present the Grouping Singular Value Decomposition (G-SVD), a novel approach which first groups similar image patches as input and then characterizes lower-dimensional regions in input space where the data density is peaked. By adding a constraint formulated via G-SVD into inverse halftoning, noises are separated from meaningful contents and similarity of nonlocal image patches is promoted. Our experiments shown that the proposed approach could improve the visual quality of reconstructed results and outperformed the state of the arts in terms of both objective and subjective measurements.