Ancient Painting to Natural Image: A New Solution for Painting Processing

Qiao, Tingting; Zhang, Weijing; Zhang, Miao; Ma, Zixuan; Xu, Dan

doi:10.1109/wacv.2019.00061

Cited by 22 publications

(11 citation statements)

References 28 publications

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“…The Artistic-Faces dataset contains artistic portraits of 16 different artists and there are only 10 images per artist available. For the landscape, we use the CLP dataset [29] that contains thousands of landscape photos as the source and 10 pencil landscape drawings as the target. Evaluated methods.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore one conjecture is that the performance of the propose method will decrease when the source and target domain become more dissimilar. To validate this, we select the FFHQ [16] face dataset as the source domain and several target domains for adaptation according to their dissimilarity with FFHQ: the CelebA-Female face [24], the emoji face [11], the cat face [3], and the color pencil landscape [29].…”

Section: Discussionmentioning

confidence: 99%

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Few-shot Image Generation with Elastic Weight Consolidation

Li,

Zhang,

et al. 2020

Preprint

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Few-shot image generation seeks to generate more data of a given domain, with only few available training examples. As it is unreasonable to expect to fully infer the distribution from just a few observations (e.g., emojis), we seek to leverage a large, related source domain as pretraining (e.g., human faces). Thus, we wish to preserve the diversity of the source domain, while adapting to the appearance of the target. We adapt a pretrained model, without introducing any additional parameters, to the few examples of the target domain. Crucially, we regularize the changes of the weights during this adaptation, in order to best preserve the "information" of the source dataset, while fitting the target. We demonstrate the effectiveness of our algorithm by generating high-quality results of different target domains, including those with extremely few examples (e.g., ≤10). We also analyze the performance of our method with respect to some important factors, such as the number of examples and the dissimilarity between the source and target domain.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Few-shot Image Generation with Elastic Weight Consolidation

Li,

Zhang,

et al. 2020

Preprint

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show abstract

“…In our task, we want to optimize the pixel-level matching of the label and output. Previous approaches have found that adding the MAE loss is beneficial to image restoration at the pixel level [44,45]. We use the MAE loss for less blurring in the estimated sinogram:Lsinofalse(Gfalse)=Ex~Pdatafalse(xfalse),z~Pzfalse(zfalse)[‖y−Gfalse(x,zfalse)‖1], where z is the noise, G(x,z) is the estimated sinogram, and ‖x‖1 is the l1 norm of x.…”

Section: Methodsmentioning

confidence: 99%

Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging

Cai

Wang

et al. 2019

Sensors

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Limited-angle computed tomography (CT) image reconstruction is a challenging problem in the field of CT imaging. In some special applications, limited by the geometric space and mechanical structure of the imaging system, projections can only be collected with a scanning range of less than 90°. We call this kind of serious limited-angle problem the ultra-limited-angle problem, which is difficult to effectively alleviate by traditional iterative reconstruction algorithms. With the development of deep learning, the generative adversarial network (GAN) performs well in image inpainting tasks and can add effective image information to restore missing parts of an image. In this study, given the characteristic of GAN to generate missing information, the sinogram-inpainting-GAN (SI-GAN) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction. We propose the U-Net generator and patch-design discriminator in SI-GAN to make the network suitable for standard medical CT images. Furthermore, we propose a joint projection domain and image domain loss function, in which the weighted image domain loss can be added by the back-projection operation. Then, by inputting a paired limited-angle/180° sinogram into the network for training, we can obtain the trained model, which has extracted the continuity feature of sinogram data. Finally, the classic CT reconstruction method is used to reconstruct the images after obtaining the estimated sinograms. The simulation studies and actual data experiments indicate that the proposed method performed well to reduce the serious artifacts caused by ultra-limited-angle scanning.

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“…Previous research shows that mean absolute error (MAE) loss is commonly used in CycleGAN, which is conducive to pixel-level image approximation (35,44). Thus, the projection domain loss function is constructed based on the MAE loss, and it is expressed as:…”

Section: Projection Domain Lossmentioning

confidence: 99%

One half-scan dual-energy CT imaging using the Dual-domain Dual-way Estimated Network (DoDa-Net) model

Wang¹,

Cai²,

Liang³

et al. 2022

Quant Imaging Med Surg

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Background: Compared with single-energy computed tomography (CT), dual-energy CT (DECT) can distinguish materials better. However, most DECT reconstruction theories require two full-scan projection datasets of different energies, and this requirement is hard to meet, especially for cases where a physical blockage disables a full circular rotation. Thus, it is critical to relax the requirements of data acquisition to promote the application of DECT.Methods: A flexible one half-scan DECT scheme is proposed, which acquires two projection datasets on two-quarter arcs (one for each energy). The limited-angle problem of the one half-scan DECT scheme can be solved by a reconstruction method. Thus, a dual-domain dual-way estimation network called DoDa-Net is proposed by utilizing the ability of deep learning in non-linear mapping. Specifically, the dual-way mapping Generative Adversarial Network (DM-GAN) was first designed to mine the relationship between two different energy projection data. Two half-scan projection datasets were obtained, the data of which was twice that of the original projection dataset. Furthermore, the data transformation from the projection domain to the image domain was realized by the total variation (TV)-based method. In addition, the image processing network (Im-Net) was employed to optimize the image domain data. Results:The proposed method was applied to a digital phantom and real anthropomorphic head phantom data to verify its effectiveness. The reconstruction results of the real data are encouraging and prove the proposed method's ability to suppress noise while preserving image details. Also, the experiments conducted on simulated data show that the proposed method obtains the closest results to the ground truth among the comparison methods. For low-and high-energy reconstruction, the peak signal-to-noise ratio (PSNR) of the proposed method is as high as 40.3899 dB and 40.5573 dB, while the PSNR of other methods is lower than 36.5200 dB. Compared with FBP, TV, and other GAN-based methods, the proposed method reduces root mean square error (RMSE) by, respectively, 0.0124, 0.0037, and 0.0016 for low-energy reconstruction, and 0.0102, 0.0028, and 0.0015 for high-energy reconstruction. Conclusions:The developed DoDa-Net model for the proposed one half-scan DECT scheme consists of two stages. In stage one, DM-GAN is used to realize the dual map of projection data. In stage two, the TV-based method is employed to transform the data from the projection domain to the image domain. Furthermore, the reconstructed image is processed by the Im-Net. According to the experimental results of qualitative and quantitative evaluation, the proposed method has advantages in detail preservation, indicating the potential of the proposed method in one half-scan DECT reconstruction.

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Ancient Painting to Natural Image: A New Solution for Painting Processing

Cited by 22 publications

References 28 publications

Few-shot Image Generation with Elastic Weight Consolidation

Few-shot Image Generation with Elastic Weight Consolidation

Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging

One half-scan dual-energy CT imaging using the Dual-domain Dual-way Estimated Network (DoDa-Net) model

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