An Inverse Halftoning Technique Using Modified Look-Up Tables
Fundamenta Informaticae
Neural network based method for image halftoning and inverse halftoning
Expert Systems with Applications: An International Journal
Halftone image resampling by interpolation and error-diffusion
Proceedings of the 2nd international conference on Ubiquitous information management and communication
The sigma-delta CNN with second order noise shaping property
WSEAS Transactions on Circuits and Systems
A Modified Kernels-Alternated Error Diffusion Watermarking Algorithm for Halftone Images
IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
Self Embedding Watermarking Scheme Using Halftone Image
IEICE - Transactions on Information and Systems
Speed up of the edge-based inverse halftoning algorithm using a finite state machine model approach
Computers & Mathematics with Applications
A second order Σ Δ modulation by cascaded Σ Δ CNNs
ICC'08 Proceedings of the 12th WSEAS international conference on Circuits
IEEE Transactions on Image Processing
A new inverse halftoning method using reversible data hiding for halftone images
EURASIP Journal on Advances in Signal Processing
Improved inverse halftoning using vector and texture-lookup table-based learning approach
Expert Systems with Applications: An International Journal
Digital reconstruction of halftoned color comics
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
An Inverse Halftoning Technique Using Modified Look-Up Tables
Fundamenta Informaticae
| Hi-index | 0.02 |
Halftones and other binary images are difficult to process with causing several degradation. Degradation is greatly reduced if the halftone is inverse halftoned (converted to grayscale) before scaling, sharpening, rotating, or other processing. For error diffused halftones, we present (1) a fast inverse halftoning algorithm and (2) a new multiscale gradient estimator. The inverse halftoning algorithm is based on anisotropic diffusion. It uses the new multiscale gradient estimator to vary the tradeoff between spatial resolution and grayscale resolution at each pixel to obtain a sharp image with a low perceived noise level. Because the algorithm requires fewer than 300 arithmetic operations per pixel and processes 7×7 neighborhoods of halftone pixels, it is well suited for implementation in VLSI and embedded software. We compare the implementation cost, peak signal to noise ratio, and visual quality with other inverse halftoning algorithms