A crisscross checking technique for tamper detection in halftone images
Journal of Systems and Software
Neural network based method for image halftoning and inverse halftoning
Expert Systems with Applications: An International Journal
Speed up of the edge-based inverse halftoning algorithm using a finite state machine model approach
Computers & Mathematics with Applications
Iterated conditional modes for inverse dithering
Signal Processing
Adaptive energy diffusion for blind inverse halftoning
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Improved inverse halftoning using vector and texture-lookup table-based learning approach
Expert Systems with Applications: An International Journal
Parallel structure-aware halftoning
Multimedia Tools and Applications
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The authors previously proposed a look up table (LUT) based method for inverse halftoning of images. The LUT for inverse halftoning is obtained from the histogram gathered from a few sample halftone images and corresponding original images. Many of the entries in the LUT are unused because the corresponding binary patterns hardly occur in commonly encountered halftones. These are called nonexistent patterns. In this paper, we propose a tree structure which will reduce the storage requirements of an LUT by avoiding nonexistent patterns. We demonstrate the performance on error diffused images and ordered dither images. Then, we introduce LUT based halftoning and tree-structured LUT (TLUT) halftoning. Even though the TLUT method is more complex than LUT halftoning, it produces better halftones and requires much less storage than LUT halftoning. We demonstrate how the error diffusion characteristics can be achieved with this method. Afterwards, our algorithm is trained on halftones obtained by direct binary search (DBS). The complexity of TLUT halftoning is higher than the error diffusion algorithm but much lower than the DBS algorithm. Also, the halftone quality of TLUT halftoning increases if the size of the TLUT gets bigger. Thus, the halftone image quality between error diffusion and DBS will be achieved depending on the size of the tree-structure in the TLUT algorithm