FL-GAN: Feature Learning Generative Adversarial Network for High-quality Face Sketch Synthesis

2021 ◽  
Author(s):  
Lin CAO ◽  
Kaixuan LI ◽  
Kangning DU ◽  
Yanan GUO ◽  
Peiran SONG ◽  
...  
Keyword(s):  
Feature Learning ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Sketch Synthesis ◽  
Face Sketch Synthesis

scholarly journals MHGAN: Multi-Hierarchies Generative Adversarial Network for High-Quality Face Sketch Synthesis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 212995-213011
Author(s):  
Kangning Du ◽  
Huaqiang Zhou ◽  
Lin Cao ◽  
Yanan Guo ◽  
Tao Wang
Keyword(s):  
High Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Sketch Synthesis ◽  
Face Sketch Synthesis

scholarly journals Robust Face Sketch Synthesis via Generative Adversarial Fusion of Priors and Parametric Sigmoid

2018 ◽  
Author(s):  
Shengchuan Zhang ◽  
Rongrong Ji ◽  
Jie Hu ◽  
Yue Gao ◽  
Chia-Wen Lin
Keyword(s):  
Activation Function ◽  
Ethnic Origin ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
In The Wild ◽  
Sketch Synthesis ◽  
Face Sketch Synthesis ◽  
Constrained Conditions ◽  
Sigmoid Activation Function

Despite the extensive progress in face sketch synthesis, existing methods are mostly workable under constrained conditions, such as fixed illumination, pose, background and ethnic origin that are hardly to control in real-world scenarios. The key issue lies in the difficulty to use data under fixed conditions to train a model against imaging variations. In this paper, we propose a novel generative adversarial network termed pGAN, which can generate face sketches efficiently using training data under fixed conditions and handle the aforementioned uncontrolled conditions. In pGAN, we embed key photo priors into the process of synthesis and design a parametric sigmoid activation function for compensating illumination variations. Compared to the existing methods, we quantitatively demonstrate that the proposed method can work well on face photos in the wild.

scholarly journals Multi-Scale Feature Channel Attention Generative Adversarial Network for Face Sketch Synthesis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 146754-146769
Author(s):  
Jieying Zheng ◽  
Yahong Wu ◽  
Wanru Song ◽  
Ran Xu ◽  
Feng Liu
Keyword(s):  
Generative Adversarial Network ◽  
Scale Feature ◽  
Multi Scale ◽  
Adversarial Network ◽  
Sketch Synthesis ◽  
Face Sketch Synthesis

scholarly journals An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

2021 ◽  
Vol 9 (7) ◽  
pp. 691
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Chenghang Weng ◽  
Pengsheng Wang ◽  
Zhiliang Deng ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Quality Evaluation ◽  
High Efficiency ◽  
Natural Image ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Quality Evaluation ◽  
Adversarial Network ◽  
Underwater Image

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

scholarly journals CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

2021 ◽  
Vol 11 (4) ◽  
pp. 1380
Author(s):  
Yingbo Zhou ◽  
Pengcheng Zhao ◽  
Weiqin Tong ◽  
Yongxin Zhu
Keyword(s):  
Distance Learning ◽  
Feature Learning ◽  
Image Synthesis ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Public Datasets

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

scholarly journals WHITE-BOX CARTOONIZATION USING AN EXTENDED GAN FRAMEWORK

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Amey Thakur ◽  
Hasan Rizvi ◽  
Mega Satish
Keyword(s):  
Smooth Surface ◽  
Generative Models ◽  
Surface Representation ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Structure Representation ◽  
Texture Representation ◽  
Adversarial Network ◽  
Representation Structure ◽  
New Framework

In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos. The learning purposes of our system are based on three distinct representations: surface representation, structure representation, and texture representation. The surface representation refers to the smooth surface of the images. The structure representation relates to the sparse colour blocks and compresses generic content. The texture representation shows the texture, curves, and features in cartoon images. Generative Adversarial Network (GAN) framework decomposes the images into different representations and learns from them to generate cartoon images. This decomposition makes the framework more controllable and flexible which allows users to make changes based on the required output. This approach overcomes any previous system in terms of maintaining clarity, colours, textures, shapes of images yet showing the characteristics of cartoon images.

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