Inverse Halftoning Based on Edge Detection Classification

Inverse halftoning is a technique to reconstruct gray-level images from halftone images. Since image halftoning process results in information loss, inverse halftoning cannot reconstruct perfectly original gray-level images from corresponding halftone images. Consequently, several inverse halftoning methods were proposed, e. g., LIH and ELIH [1]-[2], etc. In this paper, we will first review an existing inverse halftoning technique with variance classified filtering [3]. We will replace LMS (least-mean-square) algorithm by the LS (least-square) algorithm to improve the training stage for variance classified inverse halftoning in [3]. Then we will use edge detection to classify image data instead of variance used in [3]. Experiment results show that both LS filtering and edge detection classification proposed in this paper enhance quality of output gray-level images for inverse halftoning.