Artistic style transfer using deep learning

IAEME PUBLICATION, 2020

In this paper, the proposed image style transfer methodology using the Convolutional neural network, given a random pair of images, a universal image style transfer technique extract, the image texture from a reference image to synthesize an output supported the design of a content image. Image processing algorithms supported second-order statistics however are either computationally high-priced or vulnerable to generate artifacts because of the trade-off between image quality and run-time performance. Recently there has been much progress within the field of image style transfer, a process that aims at redrawing an image within the type of another image. During this paper, the proposed technique consists of a normalization step and a smoothing step. Whereas the stylization step transfers the design of the reference image to the content photograph, the smoothing step ensures spatially consistent stylizations. Every one of the steps includes a closed-form solution and maybe computed efficiently. This paper tends to conduct extensive experimental validations. The results show that the proposed technique generates photorealistic stylization outputs that are additional most popular by human subjects as compared to those by the competitive strategies, whereas, running a lot of faster.

Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced online iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. This is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful representations of both color and luminance channels, and performs stylization hierarchically with multiple losses of increasing scales. Compared to state-of-the-art networks, our network can also perform style transfer in nearly realtime by conducting much more sophisticated training offline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not just large-scale, obvious style cues but also subtle, exquisite ones. That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.

The Visual Computer, 2018

Image style transfer has attracted much attention in recent years. However, results produced by existing works still have lots of distortions. This paper investigates the CNN-based artistic style transfer work specifically and finds out the key reasons for distortion coming from twofold: the loss of spatial structures of content image during content-preserving process and unexpected geometric matching introduced by style transformation process. To tackle this problem, this paper proposes a novel approach consisting of a dual-stream deep convolution network as the loss network and edge-preserving filters as the style fusion model. Our key contribution is the introduction of an additional similarity loss function that constrains both the detail reconstruction and style transfer procedures. The qualitative evaluation shows that our approach successfully suppresses the distortions as well as obtains faithful stylized results compared to state-of-the-art methods.

IEEE Access

Transferring artistic styles onto any image or photograph has become popular in industry and academia in recent years. The use of neural style transfer (NST) for image style transfer is getting more popular. Convolution Neural Networks (CNN) based style transfer provides a new edge and life to the images, videos, and games. The re-rendering procedure of the content of one image with the style of another using various models and approaches is widely used for image style transfer. However, there are many drawbacks, including image quality, enormous loss, unrealistic artefacts, and the style of localized regions being less compared to the desired artistic style. For the reason that transfer technique fails to capture detailed, miniature textures and keep the true artwork's texture scales. We propose a multimodal CNN that stylizes hierarchically with several losses of increasing sizes while considering faithful representations of both colour and luminance channels. We may transfer not only large-scale, evident style cues but also subtle, exquisite ones by effectively handling style and texture cues at different sizes using various modalities. Our approach providing aesthetically pleasing results and is more comparable to multiple desirable creative styles using colour and texture cues at different scales. INDEX TERMS Convolution neural network, deep learning, image proccesing, neural networks, neural style transfer.

IRJET, 2021

Style transfer is an example of image stylization, an image processing and manipulation technique that's been studied for numerous decades within the broader field of non-photorealistic rendering. Style transfer is a popular computer vision technique that aims to transfer visual styles to specific content images. This is implemented by generating the output image such that it preserves some notions of the content image while adapting to certain characteristics of the style image. These characteristics are extracted from the images using a convolutional neural network. In this paper, we aim to implement a loss function that will minimize the distance between the generated image and extracted content and style representations.

Bulletin of Electrical Engineering and Informatics, 2024

Recently neural style transfer (NST) has drawn a lot of interest of researchers, with notable advancements in color representation, texture, speed, and image quality. While previous studies focused on transferring artistic style across entire content images, a new approach proposes to transfer style specifically to objects within the content image based on the style image and maintain photorealism. Recent techniques have produced intriguing creative effects, but often only work with artificial effects, leaving real flaws visible in photographs used as references for styles. The suggested approach employs a two-dimensional wavelet transform (WT) to achieve style transfer by adjusting image structure with high-pass and low pass filters (LPF). Preserving the information content and numerical attributes of VGGNet19 through WT-based style transfer using the db5 WT at level 5, we can achieve a peak signal-to-noise ratio (PSNR) value of up to 96.76725. The qualitative result of the proposed methodology is compared with other existing algorithm. Also, the time complexity of the proposed methodology on different hardware platforms has been calculated and presented in the paper. The proposed methodology able to maintains appealing and precise quality of resultant image.

The Visual Computer

This paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style image are often applied inappropriately to the content image, ignoring its semantic layout and ruining the transfer result. In order to reduce or avoid such effects, we propose a novel method based on automatically segmenting the objects and extracting their soft semantic masks from the style and content images, in order to preserve the structure of the content image while having the style transferred. Each soft mask of the style image represents a specific part of the style image, corresponding to the soft mask of the content image with the same semantics. Both the soft masks and source images are provided as multichannel input to an augmented deep CNN framework for style transfer which incorporates a generative Markov random field model. The results on various images show that our method outperforms the most recent techniques. Keywords Deep neural networks • Style transfer • Soft mask • Semantic segmentation 1 Introduction Style transfer is a process of migrating a style from a "style image" to a "content image". The goal is to be able to generate different renditions of the same scene according to different style images. Image style transfer has become a popular problem in computer vision and graphics and can generate impressive results covering a wide variety of styles for both images [16] and videos [35]. It has also been widely employed to solve problems such as texture synthe

arXiv (Cornell University), 2022

Image style transfer has attracted widespread attention in the past few years. Despite its remarkable results, it requires additional style images available as references, making it less flexible and inconvenient. Using text is the most natural way to describe the style. More importantly, text can describe implicit abstract styles, like styles of specific artists or art movements. In this paper, we propose a text-driven image style transfer (TxST) that leverages advanced image-text encoders to control arbitrary style transfer. We introduce a contrastive training strategy to effectively extract style descriptions from the image-text model (i.e., CLIP), which aligns stylization with the text description. To this end, we also propose a novel and efficient attention module that explores cross-attentions to fuse style and content features. Finally, we achieve an arbitrary artist-aware image style transfer to learn and transfer specific artistic characters such as Picasso, oil painting, or a rough sketch. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods on both image and textual styles. Moreover, it can mimic the styles of one or many artists to achieve attractive results, thus highlighting a promising direction in image style transfer.

IRJET, 2021

In the era, where colors and style fascinate everyone, more emphasis is given on aesthetics and beauty. This research paper proposes a deep learning method based on Convolutional Neural Network (CNN) to develop an application for converting images into artistic style, colorization of the image, and inpainting of image. The proposed method combines all the three applications into a single web-based application termed as Neuron. Here, colorization is performed by CNN, image inpainting is obtained by Generative Adversarial Network (GAN), style image is generated by Neural Style Transfer (NST) techniques. We trained the distinct models for all three applications and produced qualitative and quantitative comparisons with other traditional approaches to endorse this approach.

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

The success of training deep Convolutional Neural Networks (CNNs) heavily depends on a significant amount of labelled data. Recent research has found that neural style transfer algorithms can apply the artistic style of one image to another image without changing the latter's high-level semantic content, which makes it feasible to employ neural style transfer as a data augmentation method to add more variation to the training dataset. The contribution of this paper is a thorough evaluation of the effectiveness of the neural style transfer as a data augmentation method for image classification tasks. We explore the state-of-the-art neural style transfer algorithms and apply them as a data augmentation method on Caltech 101 and Caltech 256 dataset, where we found around 2% improvement from 83% to 85% of the image classification accuracy with VGG16, compared with traditional data augmentation strategies. We also combine this new method with conventional data augmentation approaches to further improve the performance of image classification. This work shows the potential of neural style transfer in computer vision field, such as helping us to reduce the difficulty of collecting sufficient labelled data and improve the performance of generic image-based deep learning algorithms.