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

2019 ◽  
Vol 9 (5) ◽  
pp. 841 ◽  
Author(s):  
Erhu Zhang ◽  
Yan Zhang ◽  
Jinghong Duan
Keyword(s):  
Image Quality ◽  
Extreme Learning Machine ◽  
Color Image ◽  
Simple Extension ◽  
Common Pattern ◽  
Look Up Table ◽  
Inverse Halftoning ◽  
Learning Machine ◽  
Halftone Images ◽  
Color Components

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.

scholarly journals Improved Extreme Learning Machine and Its Application in Image Quality Assessment

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Li Mao ◽  
Lidong Zhang ◽  
Xingyang Liu ◽  
Chaofeng Li ◽  
Hong Yang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Extreme Learning Machine ◽  
Image Quality Assessment ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Empirical Risk ◽  
Weighted Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is a new class of single-hidden layer feedforward neural network (SLFN), which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM. However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting troubles, in this paper, we incorporate the structural risk minimization principle into the (weighted) ELM, and propose a modified (weighted) extreme learning machine (M-ELM and M-WELM). Experimental results show that our proposed M-WELM outperforms the current reported extreme learning machine algorithm in image quality assessment.

scholarly journals Watermarking in dot-diffusion halftones using adaptive class-matrix and error diffusion

2019 ◽  
Vol 13 (1) ◽  
pp. 1-8
Author(s):  
Jing-Ming Guo ◽  
Sankarasrinivasan Seshathiri
Keyword(s):  
Image Quality ◽  
Color Image ◽  
Error Diffusion ◽  
Effective Strategy ◽  
Digital Halftoning ◽  
Halftone Image ◽  
Processing Order ◽  
Decoding Accuracy ◽  
Superior Image Quality ◽  
Halftone Images

Digital halftoning deals with transforming a gray or color image into its binary version which is useful in printing applications. Dot diffusion is one of the prominent halftone methods which can yield superior image quality with parallel processing capabilities. In this paper, a rapid watermarking algorithm is proposed for dot-diffusion halftone images using adaptive class-matrix selection and modified error diffusion kernels. To process the image using an adaptive class matrix, the processing order of the class matrix is reversed and transposed, and for error diffusion the coefficients are replaced with different weights. For decoding, an effective strategy is proposed based on a correlation analysis and halftone statistics. The proposed strategy can successfully embed and decode the binary watermark from a single dot-diffused halftone image. From the experimental results, the proposed method is found to be effective in terms of good decoding accuracy, imperceptibility and robustness against various printed distortions.

NMF-Based Image Quality Assessment Using Extreme Learning Machine

2017 ◽  
Vol 47 (1) ◽  
pp. 232-243 ◽  
Author(s):  
Shuigen Wang ◽  
Chenwei Deng ◽  
Weisi Lin ◽  
Guang-Bin Huang ◽  
Baojun Zhao
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Extreme Learning Machine ◽  
Image Quality Assessment ◽  
Learning Machine
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