Feature Fusion from Multiple Paintings for Generalized Artistic Style Transfer

2019 ◽  
Author(s):  
Utsav Krishnan ◽  
Akshal Sharma ◽  
Pratik Chattopadhyay
Keyword(s):  
Feature Fusion ◽  
Style Transfer ◽  
Artistic Style

scholarly journals SAR Ship Detection Based on Improved Libra RetinaNet

2021 ◽  
Author(s):  
Haomiao Liu ◽  
Haizhou Xu ◽  
Lei Zhang ◽  
Weigang Lu ◽  
Fei Yang ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Weather Conditions ◽  
Maritime Transportation ◽  
Detection Methods ◽  
Sar Images ◽  
Style Transfer ◽  
Ship Detection ◽  
Multi Scale ◽  
Optical Images ◽  
Average Accuracy

Maritime ship monitoring plays an important role in maritime transportation. Fast and accurate detection of maritime ship is the key to maritime ship monitoring. The main sources of marine ship images are optical images and synthetic aperture radar (SAR) images. Different from natural images, SAR images are independent to daylight and weather conditions. Traditional ship detection methods of SAR images mainly depend on the statistical distribution of sea clutter, which leads to poor robustness. As a deep learning detector, RetinaNet can break this obstacle, and the problem of imbalance on feature level and objective level can be further solved by combining with Libra R-CNN algorithm. In this paper, we modify the feature fusion part of Libra RetinaNet by adding a bottom-up path augmentation structure to better preserve the low-level feature information, and we expand the dataset through style transfer. We evaluate our method on the publicly available SAR dataset of ship detection with complex backgrounds. The experimental results show that the improved Libra RetinaNet can effectively detect multi-scale ships through expansion of the dataset, with an average accuracy of 97.38%.

scholarly journals Interactive Artistic Multi-style Transfer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiaohui Wang ◽  
Yiran Lyu ◽  
Junfeng Huang ◽  
Ziying Wang ◽  
Jingyan Qin
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Deep Neural Networks ◽  
Subjective Evaluation ◽  
User Interaction ◽  
Evaluation Framework ◽  
Transfer System ◽  
Style Transfer ◽  
Artistic Style ◽  
Artistic Styles

AbstractArtistic style transfer is to render an image in the style of another image, which is a challenge problem in both image processing and arts. Deep neural networks are adopted to artistic style transfer and achieve remarkable success, such as AdaIN (adaptive instance normalization), WCT (whitening and coloring transforms), MST (multimodal style transfer), and SEMST (structure-emphasized multimodal style transfer). These algorithms modify the content image as a whole using only one style and one algorithm, which is easy to cause the foreground and background to be blurred together. In this paper, an iterative artistic multi-style transfer system is built to edit the image with multiple styles by flexible user interaction. First, a subjective evaluation experiment with art professionals is conducted to build an open evaluation framework for style transfer, including the universal evaluation questions and personalized answers for ten typical artistic styles. Then, we propose the interactive artistic multi-style transfer system, in which an interactive image crop tool is designed to cut a content image into several parts. For each part, users select a style image and an algorithm from AdaIN, WCT, MST, and SEMST by referring to the characteristics of styles and algorithms summarized by the evaluation experiments. To obtain richer results, the system provides a semantic-based parameter adjustment mode and the function of preserving colors of content image. Finally, case studies show the effectiveness and flexibility of the system.

Portrait-Aware Artistic Style Transfer

2018 ◽  
Author(s):  
Yeli Xing ◽  
Jiawei Li ◽  
Tao Dai ◽  
Qingtao Tang ◽  
Li Niu ◽  
...  
Keyword(s):  
Style Transfer ◽  
Artistic Style

scholarly journals Digital Image Art Style Transfer Algorithm Based on CycleGAN

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Xuhui Fu
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Image Size ◽  
Image Detection ◽  
Style Transfer ◽  
Artistic Style ◽  
Multi Scale ◽  
The Core ◽  
Artificial Intelligence Technology ◽  
Art Style

With the continuous development and popularization of artificial intelligence technology in recent years, the field of deep learning has also developed relatively rapidly. The application of deep learning technology has attracted attention in image detection, image recognition, image recoloring, and image artistic style transfer. Some image art style transfer techniques with deep learning as the core are also widely used. This article intends to create an image art style transfer algorithm to quickly realize the image art style transfer based on the generation of confrontation network. The principle of generating a confrontation network is mainly to change the traditional deconvolution operation, by adjusting the image size and then convolving, using the content encoder and style encoder to encode the content and style of the selected image, and by extracting the content and style features. In order to enhance the effect of image artistic style transfer, the image is recognized by using a multi-scale discriminator. The experimental results show that this algorithm is effective and has great application and promotion value.

Comparing optimization methods for deep learning in image processing applications

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alexander Geng ◽  
Ali Moghiseh ◽  
Claudia Redenbach ◽  
Katja Schladitz
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Optimization Methods ◽  
Optimization Method ◽  
Stochastic Gradient Descent ◽  
Style Transfer ◽  
Artistic Style ◽  
Deep Learning Network ◽  
The Individual ◽  
The Impact

Abstract Training a deep learning network requires choosing its weights such that the output minimizes a given loss function. In practice, stochastic gradient descent is frequently used for solving the optimization problem. Several variants of this approach have been suggested in the literature. We study the impact of the choice of the optimization method on the outcome of the learning process at the example of two image processing applications from quite different fields. The first one is artistic style transfer, where the content of one image is combined with the style of another one. The second application is a real world classification task from industry, namely detecting defects in images of air filters. In both cases, clear differences between the results of the individual optimization methods are observed.

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