New inverse halftoning using texture-and lookup table-based learning approach

Inverse halftoning (IH) is used to reconstruct the gray image from an input halftone image. This paper presents a new texture-and lookup table-based (TLUT-based) IH (TLIH) algorithm to improve the quality of the reconstructed image. In the proposed TLUT-based approach, a DCT-based learning scheme is utilized to classify the training set into several kinds of textures. These classified textures are useful to build up the texture-based lookup table which is used to reconstruct high quality gray images. Under thirty real training images, experimental results demonstrated that the proposed TLIH algorithm has 1.13 dB and 0.75 dB image quality improvement when compared to the currently published two methods, one by Mese and Vaidyanathan and the other by Chung and Wu, respectively.