scholarly journals LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images

2020 ◽  
Author(s):  
Shan Lin ◽  
Fangbo Qin ◽  
Yangming Li ◽  
Randall A. Bly ◽  
Kris S. Moe ◽  
...  
Keyword(s):  
Generative Adversarial Network ◽  
Endoscopic Images ◽  
Adversarial Network ◽  
Image Translation

scholarly journals Multimodal Structure-Consistent Image-to-Image Translation

2020 ◽  
Vol 34 (07) ◽  
pp. 11490-11498
Author(s):  
Che-Tsung Lin ◽  
Yen-Yi Wu ◽  
Po-Hao Hsu ◽  
Shang-Hong Lai
Keyword(s):  
Data Augmentation ◽  
Network Models ◽  
Detection Accuracy ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Image Objects ◽  
Perceptual Distance ◽  
Adversarial Network ◽  
Image Translation ◽  
Realistic Images

Unpaired image-to-image translation is proven quite effective in boosting a CNN-based object detector for a different domain by means of data augmentation that can well preserve the image-objects in the translated images. Recently, multimodal GAN (Generative Adversarial Network) models have been proposed and were expected to further boost the detector accuracy by generating a diverse collection of images in the target domain, given only a single/labelled image in the source domain. However, images generated by multimodal GANs would achieve even worse detection accuracy than the ones by a unimodal GAN with better object preservation. In this work, we introduce cycle-structure consistency for generating diverse and structure-preserved translated images across complex domains, such as between day and night, for object detector training. Qualitative results show that our model, Multimodal AugGAN, can generate diverse and realistic images for the target domain. For quantitative comparisons, we evaluate other competing methods and ours by using the generated images to train YOLO, Faster R-CNN and FCN models and prove that our model achieves significant improvement and outperforms other methods on the detection accuracies and the FCN scores. Also, we demonstrate that our model could provide more diverse object appearances in the target domain through comparison on the perceptual distance metric.

scholarly journals Accurate and Consistent Image-to-Image Conditional Adversarial Network

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 395 ◽  
Author(s):  
Naeem Ul Islam ◽  
Sungmin Lee ◽  
Jaebyung Park
Keyword(s):  
Image Representation ◽  
Terrain Analysis ◽  
Generative Adversarial Network ◽  
Training Strategy ◽  
Adversarial Network ◽  
Image Translation ◽  
Successful Approach ◽  
Realistic Image ◽  
Potential Impact ◽  
Realistic Images

Image-to-image translation based on deep learning has attracted interest in the robotics and vision community because of its potential impact on terrain analysis and image representation, interpretation, modification, and enhancement. Currently, the most successful approach for generating a translated image is a conditional generative adversarial network (cGAN) for training an autoencoder with skip connections. Despite its impressive performance, it has low accuracy and a lack of consistency; further, its training is imbalanced. This paper proposes a balanced training strategy for image-to-image translation, resulting in an accurate and consistent network. The proposed approach uses two generators and a single discriminator. The generators translate images from one domain to another. The discriminator takes the input of three different configurations and guides both the generators to generate realistic images in their corresponding domains while ensuring high accuracy and consistency. Experiments are conducted on different datasets. In particular, the proposed approach outperforms the cGAN in realistic image translation in terms of accuracy and consistency in training.

Semi Supervised Generative Adversarial Network for Automated Glaucoma Diagnosis with Stacked Discriminator Models

2021 ◽  
Vol 11 (5) ◽  
pp. 1334-1340
Author(s):  
K. Gokul Kannan ◽  
T. R. Ganesh Babu
Keyword(s):  
Network Architecture ◽  
Super Resolution ◽  
Unsupervised Classification ◽  
Learning Task ◽  
Generative Models ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Glaucoma Diagnosis ◽  
Image Translation

Generative Adversarial Network (GAN) is neural network architecture, widely used in many computer vision applications such as super-resolution image generation, art creation and image to image translation. A conventional GAN model consists of two sub-models; generative model and discriminative model. The former one generates new samples based on an unsupervised learning task, and the later one classifies them into real or fake. Though GAN is most commonly used for training generative models, it can be used for developing a classifier model. The main objective is to extend the effectiveness of GAN into semi-supervised learning, i.e., for the classification of fundus images to diagnose glaucoma. The discriminator model in the conventional GAN is improved via transfer learning to predict n + 1 classes by training the model for both supervised classification (n classes) and unsupervised classification (fake or real). Both models share all feature extraction layers and differ in the output layers. Thus any update in one of the model will impact both models. Results show that the semi-supervised GAN performs well than a standalone Convolution Neural Networks (CNNs) model.

scholarly journals Seg2pix: Few Shot Training Line Art Colorization with Segmented Image Data

2021 ◽  
Vol 11 (4) ◽  
pp. 1464
Author(s):  
Chang Wook Seo ◽  
Yongduek Seo
Keyword(s):  
Image Data ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Training Data ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Segmented Image ◽  
Image Translation ◽  
Segmentation Image

There are various challenging issues in automating line art colorization. In this paper, we propose a GAN approach incorporating semantic segmentation image data. Our GAN-based method, named Seg2pix, can automatically generate high quality colorized images, aiming at computerizing one of the most tedious and repetitive jobs performed by coloring workers in the webtoon industry. The network structure of Seg2pix is mostly a modification of the architecture of Pix2pix, which is a convolution-based generative adversarial network for image-to-image translation. Through this method, we can generate high quality colorized images of a particular character with only a few training data. Seg2pix is designed to reproduce a segmented image, which becomes the suggestion data for line art colorization. The segmented image is automatically generated through a generative network with a line art image and a segmentation ground truth. In the next step, this generative network creates a colorized image from the line art and segmented image, which is generated from the former step of the generative network. To summarize, only one line art image is required for testing the generative model, and an original colorized image and segmented image are additionally required as the ground truth for training the model. These generations of the segmented image and colorized image proceed by an end-to-end method sharing the same loss functions. By using this method, we produce better qualitative results for automatic colorization of a particular character’s line art. This improvement can also be measured by quantitative results with Learned Perceptual Image Patch Similarity (LPIPS) comparison. We believe this may help artists exercise their creative expertise mainly in the area where computerization is not yet capable.

scholarly journals CBNWI-50: A Deep Learning Bird Dataset for Image Translation and Resolution Improvement using Generative Adversarial Network

2019 ◽  
Vol 8 (9S) ◽  
pp. 91-102
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Western India ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Adversarial Network ◽  
Image Translation ◽  
Common Birds ◽  
Single Image Super Resolution

Generative Adversarial Networks have gained prominence in a short span of time as they can synthesize images from latent noise by minimizing the adversarial cost function. New variants of GANs have been developed to perform specific tasks using state-of-the-art GAN models, like image translation, single image super resolution, segmentation, classification, style transfer etc. However, a combination of two GANs to perform two different applications in one model has been sparsely explored. Hence, this paper concatenates two GANs and aims to perform Image Translation using Cycle GAN model on bird images and improve their resolution using SRGAN. During the extensive survey, it is observed that most of the deep learning databases on Aves were built using the new world species (i.e. species found in North America). Hence, to bridge this gap, a new Ave database, 'Common Birds of North - Western India' (CBNWI-50), is also proposed in this work.

scholarly journals TVA-GAN: Attention Guided Generative Adversarial Network For Thermal To Visible Face Transformations

2021 ◽  
Author(s):  
NAND YADAV ◽  
Satish Kumar Singh ◽  
Shiv Ram Dubey
Keyword(s):  
Single Channel ◽  
Vital Role ◽  
Generative Adversarial Networks ◽  
Learning Space ◽  
Generative Adversarial Network ◽  
Visible Image ◽  
Thermal Images ◽  
Adversarial Network ◽  
Image Translation ◽  
Optimum Solution

In the recent advancement of machine learning methods for realistic image generation and image translation, Generative Adversarial Networks (GANs) play a vital role. GAN generates novel samples that look indistinguishable from the real images. The image translation using a generative adversarial network refers to unsupervised learning. In this paper, we translate the thermal images into visible images. Thermal to Visible image translation is challenging due to the non-availability of accurate semantic information and smooth textures. The thermal images contain only single-channel, holding only the images’ luminance with less feature. We develop a new Cyclic Attention-based Generative Adversarial Network for Thermal to Visible Face transformation (TVA-GAN) by incorporating a new attention-based network. We use attention guidance with a recurrent block through an Inception module to reduce the learning space towards the optimum solution.

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