Text-Enhanced Error Diffusion Using Multiplicative Parameters and Error Scaling Factor

Jae-Hyun, Kwon; Son, Chang-Hwan; Cho, Yang-Ho; Ha, Yeong-Ho

doi:10.2352/j.imagingsci.technol.(2006)50:5(437)

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…The three subnetworks of the GCM-based residual subnetwork, IRS, and SARDS used the same training and validation datasets. For digital halftoning, the continuous-tone color images were converted into grayscale images; subsequently, error diffusion [42] was used to transform the grayscale images into halftoned images. The Floyd-Steinburg filter [1,42] was used for error diffusion.…”

Section: Training Data Collectionmentioning

confidence: 99%

“…For digital halftoning, the continuous-tone color images were converted into grayscale images; subsequently, error diffusion [42] was used to transform the grayscale images into halftoned images. The Floyd-Steinburg filter [1,42] was used for error diffusion. The Laplacian operator was applied to the grayscale images to obtain Laplacian maps.…”

Section: Training Data Collectionmentioning

confidence: 99%

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Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning

Son

2021

Applied Sciences

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Layer decomposition to separate an input image into base and detail layers has been steadily used for image restoration. Existing residual networks based on an additive model require residual layers with a small output range for fast convergence and visual quality improvement. However, in inverse halftoning, homogenous dot patterns hinder a small output range from the residual layers. Therefore, a new layer decomposition network based on the Gaussian convolution model (GCM) and a structure-aware deblurring strategy is presented to achieve residual learning for both the base and detail layers. For the base layer, a new GCM-based residual subnetwork is presented. The GCM utilizes a statistical distribution, in which the image difference between a blurred continuous-tone image and a blurred halftoned image with a Gaussian filter can result in a narrow output range. Subsequently, the GCM-based residual subnetwork uses a Gaussian-filtered halftoned image as the input, and outputs the image difference as a residual, thereby generating the base layer, i.e., the Gaussian-blurred continuous-tone image. For the detail layer, a new structure-aware residual deblurring subnetwork (SARDS) is presented. To remove the Gaussian blurring of the base layer, the SARDS uses the predicted base layer as the input, and outputs the deblurred version. To more effectively restore image structures such as lines and text, a new image structure map predictor is incorporated into the deblurring network to induce structure-adaptive learning. This paper provides a method to realize the residual learning of both the base and detail layers based on the GCM and SARDS. In addition, it is verified that the proposed method surpasses state-of-the-art methods based on U-Net, direct deblurring networks, and progressively residual networks.

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Section: Training Data Collectionmentioning

confidence: 99%

Section: Training Data Collectionmentioning

confidence: 99%

Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning

Son

2021

Applied Sciences

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“…The total number of continuous-tone color images is 500. For digital halftoning, the continuous-tone color images are converted into grayscale images, and subsequently error diffusion [28] is used to transform the grayscale images into halftoned images. The Floyd-Steinburg filter [1] is used for error diffusion.…”

Section: Training Data Collectionmentioning

confidence: 99%

Inverse halftoning through structure-aware deep convolutional neural networks

Son

2020

Signal Processing

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The primary issue in inverse halftoning is removing noisy dots on flat areas and restoring image structures (e.g., lines, patterns) on textured areas. Hence, a new structure-aware deep convolutional neural network that incorporates two subnetworks is proposed in this paper. One subnetwork is for image structure prediction while the other is for continuous-tone image reconstruction. First, to predict image structures, patch pairs comprising continuous-tone patches and the corresponding halftoned patches generated through digital halftoning are trained. Subsequently, gradient patches are generated by convolving gradient filters with the continuous-tone patches. The subnetwork for the image structure prediction is trained using the mini-batch gradient descent algorithm given the halftoned patches and gradient patches, which are fed into the input and loss layers of the subnetwork, respectively. Next, the predicted map including the image structures is stacked on the top of the input halftoned image through a fusion layer and fed into the image reconstruction subnetwork such that the entire network is trained adaptively to the image structures. The experimental results confirm that the proposed structure-aware network can remove noisy dot-patterns well on flat areas and restore details clearly on textured https://arxiv.org/ 2 areas. Furthermore, it is demonstrated that the proposed method surpasses the conventional state-of-the-art methods based on deep convolutional neural networks and locally learned dictionaries.

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Color recovery of black-and-white halftoned images via categorized color-embedding look-up tables

Son

Choo

2014

Digital Signal Processing

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Text-Enhanced Error Diffusion Using Multiplicative Parameters and Error Scaling Factor

Cited by 10 publications

References 8 publications

Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning

Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning

Inverse halftoning through structure-aware deep convolutional neural networks

Color recovery of black-and-white halftoned images via categorized color-embedding look-up tables

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