Wavelet-based inverse halftoning for error diffused halftones

Djebbouri, M.; Djebouri, D.; Naoum, Reyadh

doi:10.1016/j.aeue.2004.11.039

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…They have been successfully applied in inverse halftoning at that time, but are limited to the requirement of the prior knowledge of a specific halftoning technology. Considering that wavelet transform can effectively separate and fuse high-frequency and lowfrequency image information, inverse halftoning methods based on wavelet transform are developed [9][10][11][12]. They could restore better inverse halftone images, but their processing speed is too low.…”

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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“…Its main application is processing the halftoning image, such as compression, enhancement restoration and zooming [1]. At present the frequently-used methods of inverse halftoning include look-up-table, error diffused, maximum a posteriori, wavelet transform and median pyramid transform [2][3][4][5][6]. Most of current inverse halftoning methods aim at gray image, rarely relate to color image.…”

Section: Introductionmentioning

confidence: 99%

Based on Median Pyramid Transform of the Color Image Inverse Halftoning

Zhong

Zhou

2012

AMM

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In order to eliminate the halftone noise and preserve the fine edge structure features of the color printing image, a method of color printing scanned image inverse halftoning based on median pyramid transform is proposed. Firstly the scanned images were decomposed by the transform in one scale. Then the noise distribution characteristics with approximations image and details image were analyzed and median filter was applied to denoise for details image. Then an inverse halftoning image was reconstructed from the cleaned details image and approximations image. Experimental results demonstrate that algorithm has better noise separate ability and has application value to color printing screening image restoration.

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“…The scanned or copied image is usually in halftone format no matter how meticulous and sensitive the sensor is, because the document is printed in halftoning format finally. So far, there are many halftoning schemes [1,2,8,9,12,13,15,16,18,24,28] and inverse halftoning schemes [3,5,6,10,14,21,22,27,29] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Followed by halftoning schemes, inverse halftoning schemes are developed [3,5,6,10,14,[20][21][22]29]. According to the demand for the generated halftone image, the inverse halftoning schemes can be classified into blind [5,6,21,22,29] and non-blind [14] Generally, a non-blind inverse halftoning scheme behaves better in its own settings, but a blind inverse halftoning scheme is capable for all halftone images.…”

Section: Introductionmentioning

confidence: 99%

“…According to the demand for the generated halftone image, the inverse halftoning schemes can be classified into blind [5,6,21,22,29] and non-blind [14] Generally, a non-blind inverse halftoning scheme behaves better in its own settings, but a blind inverse halftoning scheme is capable for all halftone images. Except blind and non-blind, the schemes can also be classified into frequency domain [6,22,29] and spatial domain [5,18]. Schemes for frequency domain transform the halftone images to frequency domain, then adjust the frequency, and apply the inverse transform to get a continuous tone image.…”

Section: Introductionmentioning

confidence: 99%

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Halftone image resampling by interpolation and error-diffusion

Feng

Lin

Chu

2008

Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication

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Halftoning schemes are developed for reserving the quality after transforming continuous tone images to binary images under human vision. It is wildly used in printing and outputting. So far most of halftoning schemes focus on the algorithms for halftoning transformation and inverse halftoning transformation. However, the resampling algorithm for halftone images is still insufficient, only a few research focusing on shirking halftone images. In this paper, we propose a new halftone image resampling scheme based on interpolation and error-diffusion. The proposed scheme can directly shrink and magnify halftone images to any desired scale. It does not need to transform back and forth form continuous-tone images anymore. In the experimental results, the quality of halftone image is superior to previous works.

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Wavelet-based inverse halftoning for error diffused halftones

Cited by 9 publications

References 12 publications

An inverse halftoning method based on supervised deep convolutional neural network

An inverse halftoning method based on supervised deep convolutional neural network

Based on Median Pyramid Transform of the Color Image Inverse Halftoning

Halftone image resampling by interpolation and error-diffusion

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