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
Inverse-Halftoning for Error Diffusion Based on Statistical Mechanics of the Spin System
Neural Information Processing
Steganalysis of halftone image using inverse halftoning
Signal Processing
IEEE Transactions on Image Processing
A new inverse halftoning method using reversible data hiding for halftone images
EURASIP Journal on Advances in Signal Processing
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
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
Computer-Aided reclamation of lost art
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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Two different approaches in the inverse halftoning of error-diffused images are considered. The first approach uses linear filtering and statistical smoothing that reconstructs a gray-scale image from a given error-diffused image. The second approach can be viewed as a projection operation, where one assumes the error diffusion kernel is known, and finds a gray-scale image that will be halftoned into the same binary image. Two projection algorithms, viz., minimum mean square error (MMSE) projection and maximum a posteriori probability (MAP) projection, that differ on the way an inverse quantization step is performed, are developed. Among the filtering and the two projection algorithms, MAP projection provides the best performance for inverse halftoning. Using techniques from adaptive signal processing, we suggest a method for estimating the error diffusion kernel from the given halftone. This means that the projection algorithms can be applied in the inverse halftoning of any error-diffused image without requiring any a priori information on the error diffusion kernel. It is shown that the kernel estimation algorithm combined with MAP projection provide the same performance in inverse halftoning compared to the case where the error diffusion kernel is known