Improved inverse halftoning using vector and texture-lookup table-based learning approach

► We build up t a LUT which maps an input halftone pattern to a subimage based on texture classification. ► We propose a Gaussian-based estimation to obtain a gray image by synthesizing the mapped subimages. ► Experimental results demonstrate the quality advantage of the proposed method. Lookup table-based inverse halftoning (LiH) is a popular approach to reconstruct the gray image from an input halftone image. In this paper, two improved LiH algorithms are presented. We first present a vector- and lookup table-based (VLUT-based) IH algorithm, called the VLIH algorithm, to improve the image quality of the previous LiH algorithm. Different from the previous LiH algorithm which only utilizes the gray value of each pixel to build up the LUT, our proposed VLIH algorithm considers both the gray value of each pixel and its eight neighboring pixels to build up the VLUT. Combining the proposed VLUT and the DCT-based learning scheme, an efficient texture-based VLUT (TVLUT) is built up and it constitutes the kernel of the second proposed IH algorithm called the TVLIH algorithm. Under thirty training images, with satisfactory execution-time requirement, experimental results demonstrate the quality advantage of our proposed VLIH and TVLIH algorithms when compared to the previously published three LiH algorithms.