Neural net classification and LMS reconstruction to halftone images

Pao-Chi Chang; Che-Sheng Yu

doi:10.1117/12.298373

9 January 1998 Neural net classification and LMS reconstruction to halftone images

Pao-Chi Chang, Che-Sheng Yu

Proceedings Volume 3309, Visual Communications and Image Processing '98; (1998) https://doi.org/10.1117/12.298373
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States

Abstract

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 1D correlation of halftone images and processed with a three- layer back propagation neural network. This classification method reached 100 percent 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 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.

Citation Download Citation

Pao-Chi Chang and Che-Sheng Yu "Neural net classification and LMS reconstruction to halftone images", Proc. SPIE 3309, Visual Communications and Image Processing '98, (9 January 1998); https://doi.org/10.1117/12.298373

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