Neural net classification and LMS reconstruction to halftone images

Pao Chi Chang, Che Sheng Yu

Research output: Contribution to journalConference articlepeer-review

21 Scopus citations


The objective of this work is to reconstruct high quality gray-level images from halftone images, or the inverse halftoning process. We develop high performance halftone reconstruction methods for several commonly used halftone techniques. For better reconstruction quality, image classification based on halftone techniques is placed before the reconstruction process so that the halftone reconstruction process can be fine tuned for each halftone technique. The classification is based on enhanced one-dimensional correlation of halftone images and processed with a three-layer back propagation neural network. This classification method reached 100% accuracy with a limited set of images processed by dispersed-dot ordered dithering, clustered-dot ordered dithering, constrained average, and error diffusion methods in our experiments. For image reconstruction, we apply the least-mean-square (LMS) adaptive filtering algorithm which intends to discover the optimal filter weights and the mask shapes. As a result, it yields very good reconstruction image quality. The error diffusion yields the best reconstructed quality among the halftone methods. In addition, the LMS method generates optimal image masks which are significantly different for each halftone method. These optimal masks can also be applied to more sophisticated reconstruction methods as the default filter masks.

Original languageEnglish
Pages (from-to)592-602
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Issue number2
StatePublished - 1998
EventVisual Communications and Image Processing '98 - San Jose, CA, United States
Duration: 28 Jan 199830 Jan 1998


  • Halftone
  • Image reconstruction
  • Inverse halftoning
  • LMS adaptive filter
  • Neural networks


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