Complementary, Heterogeneous and Adversarial Networks for Image-to-Image Translation

Gao, Fei; Xu, Xingxin; Yu, Jun; Shang, Meimei; Li, Xiang; Tao, Dacheng

doi:10.1109/tip.2021.3061286

Cited by 21 publications

(2 citation statements)

References 49 publications

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“…The generator is trained to generate a fake image that is indistinguishable from the ground truth image, and the discriminator is trained to better detect fake images, so that the generated image can be optimized in detail or re-generated in the generator. 10 11 The training and validation procedures of the model were performed 100 training set of PD and T2 pairs to determine the optimal parameters. The test the trained model and predicted images were obtained.…”

Section: Methodsmentioning

confidence: 99%

Synthesis of T2-weighted images from proton density images using a generative adversarial network in a temporomandibular joint magnetic resonance imaging protocol

Lee

Choi

et al. 2022

Imaging Sci Dent

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Purpose This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint (TMJ) magnetic resonance imaging (MRI) protocol. Materials and Methods From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen’s ĸ coefficient. Results The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement (ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement (ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. Conclusion The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

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Section: Methodsmentioning

confidence: 99%

Synthesis of T2-weighted images from proton density images using a generative adversarial network in a temporomandibular joint magnetic resonance imaging protocol

Lee

Choi

et al. 2022

Imaging Sci Dent

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“…In this paper, we apply a simple, yet effective deep denoising prior network, which is shown in Figure 2. Inspired by the huge success of UNet in the field of image-to-image translation [24,25], we adopted an encoder-decoder structure as our denoising network backbone. The network contains two downsampling steps and two upsampling steps.…”

Section: Deep Denoised Priormentioning

confidence: 99%

Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection

Qiu

Niu

et al. 2022

Symmetry

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Images in real surface defect detection scenes often suffer from uneven illumination. Retinex-based image enhancement methods can effectively eliminate the interference caused by uneven illumination and improve the visual quality of such images. However, these methods suffer from the loss of defect-discriminative information and a high computational burden. To address the above issues, we propose a joint-prior-based uneven illumination enhancement (JPUIE) method. Specifically, a semi-coupled retinex model is first constructed to accurately and effectively eliminate uneven illumination. Furthermore, a multiscale Gaussian-difference-based background prior is proposed to reweight the data consistency term, thereby avoiding the loss of defect information in the enhanced image. Last, by using the powerful nonlinear fitting ability of deep neural networks, a deep denoised prior is proposed to replace existing physics priors, effectively reducing the time consumption. Various experiments are carried out on public and private datasets, which are used to compare the defect images and enhanced results in a symmetric way. The experimental results demonstrate that our method is more conducive to downstream visual inspection tasks than other methods.

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DFSGAN: Introducing editable and representative attributes for few-shot image generation

Yang

Niu²,

Wang

et al. 2023

Engineering Applications of Artificial Intelligence

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Complementary, Heterogeneous and Adversarial Networks for Image-to-Image Translation

Cited by 21 publications

References 49 publications

Synthesis of T2-weighted images from proton density images using a generative adversarial network in a temporomandibular joint magnetic resonance imaging protocol

Synthesis of T2-weighted images from proton density images using a generative adversarial network in a temporomandibular joint magnetic resonance imaging protocol

Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection

DFSGAN: Introducing editable and representative attributes for few-shot image generation

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