In order to improve the estimation precision of inverse halftoning values whose halftone patterns don’t appear in image training set

an inverse halftoning method based on look up table(LUT) and Elman recurrent network is proposed. Firstly the preliminary LUT is built from image training set. Then the Elman recurrent network is used to approximate the mapping between halftoning and inverse halftoning. After studying

training and optimizing the structure

layers and nodes of the Elman network are established appropriately. With this network the continuous-tones of nonexistent halftone patterns are estimated. And the complete LUT is generated. Experiments show that the proposed algorithm can generate inverse halftoning images of good in both visual effect and PSNR while having low memory requirement and inexpensive computation than some of existing algorithms.