Neural network based method for image halftoning and inverse halftoning

A hybrid neural network based method for halftoning and inverse halftoning of digital images is presented. The halftone image is performed by single-layer perceptron neural network (SLPNN), and its corresponding continuous-tone image is reconstructed by radial-basis function neural network (RBFNN). The combined training procedure produces halftone images and the corresponding continuous tone images at the same time. The PSNR performance and visual image quality of these contone images achieved is comparable to the well-known inverse halftoning methods. The resultant halftone images compared with the error diffusion halftone are visually good, too. Furthermore, we apply different kinds of halftone images to a bi-level image compression method, called Block Arithmetic Coding for Image Compression (BACIC), which is better than the current facsimile methods.

[1]  Niranjan Damera-Venkata,et al.  A fast, high-quality inverse halftoning algorithm for error diffused halftones , 2000, IEEE Trans. Image Process..

[2]  Jan P. Allebach,et al.  Efficient model based halftoning using direct binary search , 1997, Proceedings of International Conference on Image Processing.

[3]  Robert L. Stevenson,et al.  Inverse halftoning via MAP estimation , 1997, IEEE Trans. Image Process..

[4]  Charles G. Boncelet,et al.  An algorithm for compression of bilevel images , 2001, IEEE Trans. Image Process..

[5]  Ping Wah Wong,et al.  Inverse halftoning and kernel estimation for error diffusion , 1995, IEEE Trans. Image Process..

[6]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[7]  P. P. Vaidyanathan,et al.  Look-up table (LUT) method for inverse halftoning , 2001, IEEE Trans. Image Process..

[8]  Nguyen T. Thao.  Set theoretic inverse halftoning , 1997, Proceedings of International Conference on Image Processing.

[9]  Rodney L. Miller,et al.  Design of minimum visual modulation halftone patterns , 1991, IEEE Trans. Syst. Man Cybern..

[10]  Stephen M. Rock,et al.  Gradient‐based parameter optimization for systems containing discrete‐valued functions , 2002 .

[11]  Eve A. Riskin,et al.  Error-diffused image compression using a binary-to-gray-scale decoder and predictive pruned tree-structured vector quantization , 1994, IEEE Trans. Image Process..

[12]  Albert J. Ahumada,et al.  Principled halftoning based on human vision models , 1992, Electronic Imaging.

[13]  Jan P. Allebach,et al.  Look-up-table based halftoning algorithm , 2000, IEEE Trans. Image Process..

[14]  David L. Neuhoff,et al.  Least-squares model-based halftoning , 1992, Electronic Imaging.

[15]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation (3rd Edition) , 2007 .

[16]  Jan P. Allebach,et al.  Model-based halftoning using direct binary search , 1992, Electronic Imaging.

[17]  Pao-Chi Chang,et al.  Hybrid LMS-MMSE inverse halftoning technique , 2001, IEEE Trans. Image Process..

[18]  Avideh Zakhor,et al.  Halftone to continuous-tone conversion of error-diffusion coded images , 1995, IEEE Trans. Image Process..

[19]  Jan P. Allebach,et al.  Tone-dependent error diffusion , 2004, IEEE Transactions on Image Processing.

[20]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[21]  Michael T. Orchard,et al.  Inverse halftoning using wavelets , 1999, IEEE Trans. Image Process..

[22]  Henry R. Kang.  Digital Color Halftoning , 1999 .

[23]  P. P. Vaidyanathan,et al.  Tree-structured method for LUT inverse halftoning and for image halftoning , 2002, IEEE Trans. Image Process..

[24]  James J. Carroll,et al.  Approximation of nonlinear systems with radial basis function neural networks , 2001, IEEE Trans. Neural Networks.

[25]  Jan P. Allebach,et al.  Impact of HVS models on model-based halftoning , 2002, IEEE Trans. Image Process..

[26]  A. Enis Çetin,et al.  Restoration of error-diffused images using POCS , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).