Optimised halftoning and inverse halftoning of dermoscopic images for supporting teledermoscopy system

Ashour, Amira S.; Guo, Yanhui; Hawas, Ahmed Refaat; Du, Chunlai

doi:10.1049/iet-ipr.2018.6166

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…Common application is for binary output devices such as printers, fax machines, copiers, digital monitors, and so on. Another application is bilevel data compression, such as telemedicine and the Internet of things [1,2]. So far, various digital halftoning technologies have been proposed, such as ordered dithering, error diffusion, dot diffusion, and iterative [3,4].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

An inverse halftoning method based on supervised deep convolutional neural network

Li,

Liu

2023

IET Image Processing

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“…Thus, the digital halftone technique is widely used in bi-level output devices in order to reproduce the tone of a continuous-tone image, such as printing press machines, printers, fax machines and so on [1,2]. Besides, the digital halftone technique can also be employed as an image compression mode for saving storage space or electric power in special occasions, for example in telemedicine [3] and IoT [4]. Major halftone methods used in practice include ordered dithering (OD), dot diffusion (DD), error diffusion (ED) and direct binary search (DBS) [5].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Convolutional Neural Network Model Combined with Attention Mechanism for Inverse Halftoning

Shao

Zhang

2021

Electronics

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Inverse halftoning acting as a special image restoration problem is an ill-posed problem. Although it has been studied in the last several decades, the existing solutions can’t restore fine details and texture accurately from halftone images. Recently, the attention mechanism has shown its powerful effects in many fields, such as image processing, pattern recognition and computer vision. However, it has not yet been used in inverse halftoning. To better solve the problem of detail restoration of inverse halftoning, this paper proposes a simple yet effective deep learning model combined with the attention mechanism, which can better guide the network to remove noise dot-patterns and restore image details, and improve the network adaptation ability. The whole model is designed in an end-to-end manner, including feature extraction stage and reconstruction stage. In the feature extraction stage, halftone image features are extracted and halftone noises are removed. The reconstruction stage is employed to restore continuous-tone images by fusing the feature information extracted in the first stage and the output of the residual channel attention block. In this stage, the attention block is firstly introduced to the field of inverse halftoning, which can make the network focus on informative features and further enhance the discriminative ability of the network. In addition, a multi-stage loss function is proposed to accelerate the network optimization, which is conducive to better reconstruction of the global image. To demonstrate the generalization performance of the network for different types of halftone images, the experiment results confirm that the network can restore six different types of halftone image well. Furthermore, experimental results show that our method outperforms the state-of-the-art methods, especially in the restoration of details and textures.

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“…Different from the general binary image, the halftone image can be perceived as a continuous-tone image when it is viewed from a certain distance because of the low filtering character of the human visual system. Halftone images are widely used in many fields, such as the printing industry [1], telemedicine [2], Internet of Things (IoT) [3], and the textile industry [4]. The common application case of halftone image is for bi-level output devices such as printing press, printers, copiers, fax machines, plasma display panels, and so on.…”

Section: Introductionmentioning

confidence: 99%

“…The common application case of halftone image is for bi-level output devices such as printing press, printers, copiers, fax machines, plasma display panels, and so on. Another application field is in that the halftone image can be used as an image compression mode where there is not enough storage space and ultra-low power image acquisition is needed, such as in telemedicine and IoT [2,3]. Inverse halftoning is an inverse process of halftoning that is used to restore continuous-tone images from their halftone versions.…”

Section: Introductionmentioning

confidence: 99%

Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review

Zhang

Wang

et al. 2020

Applied Sciences

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Inverse halftoning is an ill-posed problem that refers to the problem of restoring continuous-tone images from their halftone versions. Although much progress has been achieved over the last decades, the restored images still suffer from detail loss and visual artifacts. Recent studies show that inverse halftoning methods based on deep learning are superior to other traditional methods, and thus this paper aimed to systematically review the inverse halftone methods based on deep learning, so as to provide a reference for the development of inverse halftoning. In this paper, we firstly proposed a classification method for inverse halftoning methods on the basis of the source of halftone images. Then, two types of inverse halftoning methods for digital halftone images and scanned halftone images were investigated in terms of network architecture, loss functions, and training strategies. Furthermore, we studied existing image quality evaluation including subjective and objective evaluation by experiments. The evaluation results demonstrated that methods based on multiple subnetworks and methods based on multi-stage strategies are superior to other methods. In addition, the perceptual loss and the gradient loss are helpful for improving the quality of restored images. Finally, we gave the future research directions by analyzing the shortcomings of existing inverse halftoning methods.

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Optimised halftoning and inverse halftoning of dermoscopic images for supporting teledermoscopy system

Cited by 6 publications

References 36 publications

An inverse halftoning method based on supervised deep convolutional neural network

An inverse halftoning method based on supervised deep convolutional neural network

An Efficient Convolutional Neural Network Model Combined with Attention Mechanism for Inverse Halftoning

Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review

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