A novel algorithm for inverse halftoning using LUT approach and pattern labeling

Liaghat, Alireza; Rassaei, Farshad

doi:10.1109/beiac.2012.6226044

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…They could restore better inverse halftone images, but their processing speed is too low. In order to improve the processing speed, lookup-table (LUT) is adopted to generate inverse halftone image [13][14][15][16][17]. These methods run fast because it does not require a filter, but they produce images of inconsistent quality because it is difficult to select an ideal plate.…”

Section: Related Workmentioning

confidence: 99%

An inverse halftoning method based on supervised deep convolutional neural network

Li,

Liu

2023

IET Image Processing

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Inverse halftoning is a technology that converts a binary image into a continuous tone image. Due to the wide application of inverse halftoning, many scholars have proposed several deep convolutional neural networks (DCNN) to optimize their performance. According to the observation, there is still room for improvement in content generation and detail recovery of the inverse halftone images generated by using the existing methods. Therefore, an inverse halftoning method based on supervised DCNN is proposed in this paper. The method consists of two parts: the multi‐level feature extraction model uses the down‐sampling to extract the features from the halftone image and remove the halftone noise dots on flat areas, which is implemented by four convolutional layers; the image reconstruction model uses up‐sampling to reconstruct image information, which is realized by four convolutional layers and two dense residual blocks. At the same time, in order to further recover the details, the down‐sampling feature maps and up‐sampling feature maps of the same size are concatenated by addition layers. Experimental results show that compared with other methods, the inverse halftone images obtained by the proposed network have better results in both subjective and objective evaluations.

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Section: Related Workmentioning

confidence: 99%

An inverse halftoning method based on supervised deep convolutional neural network

Li,

Liu

2023

IET Image Processing

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“…The method based on look-up table was first proposed by Mese et al [13] for inverse halftoning. Since then, many methods were proposed to improve the performance of this method [14][15][16][17][18][19][20][21][22]. In summary, the improvements include three aspects.…”

Section: Introductionmentioning

confidence: 99%

“…The first is the template selection and optimization [14,15], where the correlation method and the elitist genetic algorithm were used to obtain the best template. The second is to reduce the size of the table and improve the speed of searching look-up table [16][17][18][19], while the third is to get the accurate contone value from each template pattern [20][21][22]. Although these improvements have produced relatively satisfactory results for grey images, very little research has addressed color inverse halftoning.…”

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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“…This experiment aims to cultivate students' comprehensive ability to apply the knowledge to analyze and solve problems is the foundation of the design experiments. Comprehensive experiment is helpful for the students to form strong ability of self-study and independent work [5].…”

Section: B the Experimental Didacticsmentioning

confidence: 99%

Research on the Novel Education Pattern for Science and Engineering Classes Based on Experiment and Interactive Teaching Method

Huang¹,

Xie²,

Bian³

et al. 2016

Advances in Computer Science Research

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Abstract-In this paper, we conduct research on novel education pattern for science and engineering classes based on experiment and the interactive teaching method. Teaching ways should be further to the combination of knowledge and exploration, interaction between teachers and students teaching learning and to mobilize students' autonomous learning, stimulate student curiosity and creativity as the main goal of teaching way transformation. Classroom teaching is the main way for students to master basic theory, basic skills, to guarantee the quality of classroom teaching is in the classroom in the limited time and the space for the effective interaction of teachers and students. We believe that through a series of effective exploration and reform of teaching, it will usher in spring of higher education in the 21st century. In this context, to develop high-tech talents with international level, we will break bondage of students thought the traditional education mode. The proposed experiment and the interactive teaching method enhance the overall performance of the education process which will be beneficial to optimize the current education pattern. In the future research, we will combine more corresponding theory into the topic to modify the current research result.

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A novel algorithm for inverse halftoning using LUT approach and pattern labeling

Cited by 5 publications

References 6 publications

An inverse halftoning method based on supervised deep convolutional neural network

An inverse halftoning method based on supervised deep convolutional neural network

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

Research on the Novel Education Pattern for Science and Engineering Classes Based on Experiment and Interactive Teaching Method

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