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

Li, Mei; Zhang, Erhu; Wang, Yutong; Duan, Jinhui; Jing, Cuining

doi:10.3390/app10041521

Cited by 8 publications

(4 citation statements)

References 41 publications

(98 reference statements)

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“…However, as presented in reference [42], PSNR and SSIM fail to accurately evaluate image quality with respect to the human visual system. Therefore, we employ subjective evaluation to further assess the image quality by observing both the whole image and local details according to the method proposed in reference [43].…”

Section: Resultsmentioning

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: Resultsmentioning

confidence: 99%

An inverse halftoning method based on supervised deep convolutional neural network

Li,

Liu

2023

IET Image Processing

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“…Huajian et al [7] present an inverse halftoning method using an invertible neural network. Li et al [8] presents a review of the main inverse halftoning methods. However, our work is about increasing halftone resolution and not inverse halftoning.…”

Section: A Related Workmentioning

confidence: 99%

“…GAN-based techniques often produce output images similar to those produced by inverse halftoning [8], because they interpret halftone texture as noise, resulting in "clean" images without texture (Fig. 13).…”

Section: Comparing Halftone-edsr With Models Trained With Continuous-...mentioning

confidence: 99%

Super-Resolution of Color Halftone Images Using Convolutional Neural Networks

Novaes,

Kim

2024

IEEE Access

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In this work, we investigate how to increase the resolution of color halftone images using convolutional neural networks (CNNs). As far as we know, this is the first work that increases resolution of color halftone images using a CNN-based solution. For this task, we first train the well-known Enhanced Deep Super-Resolution (EDSR) network with halftone images to obtain the Halftone-EDSR model. We argue that it is not possible to use conventional data augmentation techniques in this problem, due to the peculiar texture of halftone images. We present cropping as a viable data augmentation technique. Using cropping and image patches as training samples, we substantially speed up the training and get better quality models. We compare the independent channel model (which increases the resolution of each of the CMYK channels independently and then merges them) with the joint channel model (which increases the resolution of all four image channels at once) and conclude that the the latter is superior to the first. We experimentally demonstrate that the proposed Halftone-EDSR is superior to all previous techniques, both for pre-print and post-print halftone images. However, Halftone-EDSR can generate upsampled images with Moiré patterns. To minimize Moiré patterns, we propose a new network model called Halftone-Net. We use the Fast Fourier Transform, followed by a CNN, to detect the strong presence of Moiré patterns in halftone images and demonstrate that Halftone-Net generates fewer images with strong Moiré patterns than Halftone-EDSR.

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“…Since 1990s, many inverse halftoning methods have been proposed. These presented methods can be roughly divided into traditional methods and deep learning-based methods [6]. The traditional methods include filters [7,8], projection onto convex sets method (POCS) [9], maximum a posteriori (MAP) estimation method [10], wavelet-based method [11], look-up-table method (LUT) [12,13], dictionary learning method [2,14], and neural networks method [15].…”

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

Cited by 8 publications

References 41 publications

An inverse halftoning method based on supervised deep convolutional neural network

An inverse halftoning method based on supervised deep convolutional neural network

Super-Resolution of Color Halftone Images Using Convolutional Neural Networks

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

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