Inverse halftoning based on edge detection classification

Ong, Qi-Xuan; Hsue, Wen-Liang

doi:10.1109/apsipa.2013.6694350

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…The proposed methods can be classified into five categories: filtering, deconvolution, optimization estimation, vector quantization, and machine learning [1,2]. The filtering-based method includes Gaussian low-pass filtering method [3], adaptive filtering [4], linear or non-linear filtering [5], and transform-domain filtering [6]. The low-pass filtering is a simple and fast inverse halftoning method, it can remove most of the noise in the reconstructed continuous tone image.…”

Section: Introductionmentioning

confidence: 99%

Color Inverse Halftoning Method with the Correlation of Multi-Color Components Based on Extreme Learning Machine

Zhang

Duan

2019

Applied Sciences

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Look-up table (LUT) based method is a popular and effective way for inverse halftoning. However, it still has very large development space to improve the reconstructed color image quality for color halftone images, because most of the existing color inverse halftoning methods are the simple extension of LUT methods to each color components separately. To this end, this paper presents a novel color inverse halftoning method by exploiting the correlation of multi-color components. Through considering all existent contone values with the same halftone pattern in three color component tables, we firstly propose a concept of common pattern. Then the extreme learning machine (ELM) is employed to estimate the contone values for nonexistent patterns according to common patterns in color LUT, which can not only improve the fitting precision of nonexistent values but also has fast transformation speed. Experimental results show that the proposed method achieves a better image quality when compared to previously published methods.

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