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Soft Computing

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04.12.2017 | Focus

Wavelet-content-adaptive BP neural network-based deinterlacing algorithm

verfasst von: Jin Wang, Jechang Jeong

Erschienen in: Soft Computing | Ausgabe 5/2018

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Abstract

In this paper, we introduce an intra-field deinterlacing algorithm based on a wavelet-content-adaptive back propagation (BP) neural network (BP-NN) using pixel classification. During interpolation, there is an issue of different image features having completely different properties, such as smooth regions, edges, and textures. We use the wavelet transform to divide the images into several pieces with different properties. Then, each piece has similar image features and each one is assigned to one neural network. The BP-NN-based deinterlacing algorithm can reduce blurring by recovering the missing pixels via a learning process. Compared with existing deinterlacing algorithms, the proposed algorithm improves the peak signal-to-noise ratio and visual quality while maintaining high efficiency.

Vorheriger Artikel Melanocytic and nevus lesion detection from diseased dermoscopic images using fuzzy and wavelet techniques

Nächster Artikel Stratified modeling in soft fuzzy topological structures

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Titel: Wavelet-content-adaptive BP neural network-based deinterlacing algorithm
verfasst von: Jin Wang
Jechang Jeong
Publikationsdatum: 04.12.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 5/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2968-x

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