Artwork style transfer model using deep learning approach

Artistic style transfer is one the enormous research fields in computer vision. It is a technique to synthesize a content image and the style together to form an stylized image. Various popular applications like Prisma, Deepart, Pikazoapp provide a platform to synthesis images in order to produce very exciting artistic style. These applications use Convolutional Neural Networks to transform the images in an artistic style. Prisma photo editor has more than 250 modern art filters and 110 million users. Hence, the amount of images being synthesized is huge and computation requires to perform the task efficiently is large. Abundant CNN architectures like VGG16, VGG19, GoogleNet etc are introduced to solve this problem of heavy computations. The system helps users to transform their images into artworks using style of famous artists for stirring image filters, and it might be useful for artists to have an indication about the art. This paper presents the fundamental concepts, classification of Style Transfer and major progress towards Neural Style Transfer.

Stylized image denoising via noise style transfer and Quasi Siamese network

Style transfer is an optimization technique used to create a new image out of a content image and a style image (as in an artistic work by a well-known painter) as a result of blending, the content image is altered to be painted in the same style as the content image but to resemble it of the style image. The essential method for creating style transfer is the convolutional neural network that allows for transfer (CNN). This paper will analyse key methods for performing style transfer photos and quickly compare multiple models and their outcomes. We primarily contrast one pioneering technique, developed by Leon Gatys in 2015, with the most recent technology in the High-Resolution Network from 2019 for the transfer of photorealistic style. Gatys's Neural Algorithm of Artistic Style produces excellent results, but the resulting data doesn't maintain the content image's characteristics and the fact that the paper gives no concise description of the internal mechanisms gram matrix. The enhancement is the conversion to a photorealistic style transfer from Gatys' neural style transfer. It aids in maintaining the structure and defining characteristics within the content image. Both methods make use of VGG, a network that was trained using the ImageNet dataset for conducting image categorization. We made our own data set with five types or groups and used transfer learning in a VGG network to do a style transfer using transfer learning.

Artistic Style Transfer(AST) a fascinating aspect of Neural Style Transfer(NST) has redefined the creative manipulation of digital images by imbuing photographs with the aesthetic characteristics of famous paintings. However a persistent challenge in NST-color distortion—often compromises the color fidelity of the content image during the style transfer process. This paper introduces an innovative approach employing the VGG-19 neural network to extract style features while preserving the color harmony of the content image through luminance transfer. This method ensures that the original color palette remains unaltered achieving an authentic blend of style and content characteristics. In our experimentation, we applied the innovative artistic style transfer technique to a variety of content images, incorporating the aesthetic elements from renowned paintings. Notably, our method successfully preserved the color fidelity of the content images throughout the style transfer process. This approach contributes significantly to the field by offering a improved technique for NST that harmonizes artistic style with natural coloration serving a valuable asset for artists and technologists.

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.

Neural style transfer represented a paradigm shift in image artistic rendering and applications in digital entertainment. Generative adversarial networks are considered as a general solution for style transfer problems. Researchers have explored multiple learning algorithms to increase the learning ability of style transfer networks. However, such research has overlooked an important outside motivator: social preference. Classical style transfer networks are trained offline and worked as a stationary model to transfer photos into stylized images, without any interaction with the environment. Based on the ideas from online training, we propose a new coarse-to-fine training strategy for neural style transfer systems to adapt to social preference change. In coarse stage, a primary model is learned via normal training method. In fine stage, the model is updated with online learning approach and sequentially added new data. We show that our approach exhibits improved performance compared to stationary model from visual effect and reflection of social preference. We conclude that the coarse-to-fine training strategy can improve the output of the generative model in social media environment.

The development of Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) has catalysed the emergence of the Neural Style Transfer (NST) field. NST elucidates the computer's comprehension of artistic elements, holding paramount significance in both academic and practical domains. In this paper, we introduce a novel system termed "Content-Adaptive Style Transfer." This system incorporates two additional techniques: object segmentation and image fusion, augmenting the customizability of NST. Our experimental results underscore its capability to execute style transfer exclusively on the foreground, leaving the background unaffected, or to modify the background style while preserving the original foreground. These innovations promise more rational outcomes in specific scenarios.

This work presents Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, that can fit in most existing arbitrary image style transfer models, such as CNN-based, ViT-based, and flow-based methods. As the key component in image style transfer tasks, a suitable style representation is essential to achieve satisfactory results. Existing approaches based on deep neural networks typically use second-order statistics to generate the output. However, these hand-crafted features computed from a single image cannot leverage style information sufficiently, which leads to artifacts such as local distortions and style inconsistency. To address these issues, we learn style representation directly from a large number of images based on contrastive learning by considering the relationships between specific styles and the holistic style distribution. Specifically, we present an adaptive contrastive learning scheme for style transfer by introducing an input-dependent temperature. Our framework consists of three key components: a parallel contrastive learning scheme for style representation and transfer, a domain enhancement (DE) module for effective learning of style distribution, and a generative network for style transfer. Qualitative and quantitative evaluations show the results of our approach are superior to those obtained via state-of-the-art methods. The code is available at https://github.com/zyxElsa/CAST_pytorch .

Despite the remarkable results and numerous advancements in neural style transfer, achieving artistic control is still a challenging feat, primarily since existing methodologies treat the style representation as a black-box model. This oversight significantly limits the range of possible artistic manipulations. In this paper, we propose a method to enable artistic control on any correlation-based style transfer models along with guiding intuitions. Our focus is on controlling two perceptual factors: Pattern Density and Stroke Strength. To achieve this, we introduce the centered Gram style representation and manipulate it with our variance-aware adaptive weighting and correlation-based selective masking. Through several experiments and comparisons with the state-of-the-art, we show that we can achieve artistic control with competitive stylization quality. Additionally, since our method involves manipulating style representation, it can easily be adapted to popular style transfer models. We analyze different style representation properties to propose rules that govern the style transfer process, which is critical towards achieving artistic control over pattern density and stroke strength.

Neural style transfer has recently received significant attention and demonstrated amazing results. An efficient solution proposed by Johnson et al. trains feed-forward convolutional neural networks by defining and optimizing perceptual loss functions. Such methods are typically based on high-level features extracted from pre-trained neural networks, where the loss functions contain two components: style loss and content loss. However, such pre-trained networks are originally designed for object recognition, and hence the high-level features often focus on the primary target and neglect other details. As a result, when input images contain multiple objects potentially at different depths, the resulting images are often unsatisfactory because image layout is destroyed and the boundary between the foreground and background as well as different objects becomes obscured. We observe that the depth map effectively reflects the spatial distribution in an image and preserving the depth map of the content image after stylization helps produce an image that preserves its semantic content. In this paper, we introduce a novel approach for neural style transfer that integrates depth preservation as additional loss, preserving overall image layout while performing style transfer.

The neural style transfer has achieved a significant improvement with deep learning methods. However, the existing methods are susceptible to lack the ability for handling the texture style transfer because of their less consideration of the textural structure from style images. To overcome this drawback, this letter presents a simple method to capture the textural structure by using an adaptive auto-correlation alignment loss function. Furthermore, we also introduce three metrics to quantitatively evaluate the performance. We qualitatively and quantitatively evaluate the proposed methods. The experimental results demonstrate the superiority of the proposed method and our method can synthesize the stylized images with rich texture style patterns.

Recently, style transfer based on the convolutional neural network has achieved remarkable results. In this paper, we extend the original neural style transfer algorithm to ameliorate the instability in the reconstruction of certain structural information, and improve the ghosting artefacts in the background of image which with low texture and homogeneous areas. For that end, we adopt zigzag learning strategy: The model parameters are optimized to an intermediate target firstly, then let the model converge to the final goal. We show the zigzag learning to multi-sample model which is fabricated from resampling the style input and to loss function that is split into two sections. And also, we demonstrate experimentally the effectiveness of the proposed algorithm and provide its theoretical analysis. Finally we show how to integrate the zigzag learning strategy in fast neural style transfer framework.

We present StyleTune, a mobile app for interactive style transfer enhancement that enables global and spatial control over stroke elements and can generate high fidelity outputs. The app uses adjustable neural style transfer (NST) networks to enable art-direction of stroke size and orientation in the output image. The implemented approach enables continuous and seamless edits through a unified stroke-size representation in the feature space of the style transfer network. StyleTune introduces a three-stage user interface, that enables users to first explore global stroke parametrizations for a chosen NST. They can then interactively locally retouch the stroke size and orientation using brush metaphors. Finally, high resolution outputs of 20 Megapixels and more can be obtained using a patch-based upsampling and local detail transfer approach, that transfers small-scale details such as paint-bristles and canvas structure. The app uses Apple’s CoreML and Metal APIs for efficient on-device processing.

In today's world, Neural Style Transfer (NST) has become a trendsetting term. NST combines two pictures, a content picture and a reference image in style (such as the work of a renowned painter) in a way that makes the output image look like an image of the material, but rendered with the form of a reference picture. However, there is no study using the artwork or painting of Bangladeshi painters. Bangladeshi painting has a long history of more than two thousand years and is still being practiced by Bangladeshi painters. This study generates NST stylized image on Bangladeshi paintings and analyzes the human point of view regarding the aesthetic preference of NST on Bangladeshi paintings. To assure our study's acceptance, we performed qualitative human evaluations on generated stylized images by 60 individual humans of different age and gender groups. We have explained how NST works for Bangladeshi paintings and assess NST algorithms, both qualitatively \& quantitatively. Our study acts as a pre-requisite for the impact of NST stylized image using Bangladeshi paintings on mobile UI/GUI and material translation from the human perspective. We hope that this study will encourage new collaborations to create more NST related studies and expand the use of Bangladeshi artworks.

Seismic data are widely used in oil, gas, and other kinds of mineral exploration and development. However, due to low artificial interpretation accuracy and small sample sizes, seismic data may not meet the needs of convolutional neural network training. There are major differences between optical image and seismic data, making it difficult for a model to learn seismic data characteristics. Therefore, a style transfer network is necessary to make the styles of optical image and seismic data more similar. Since the stylization effect of a seismic section is similar to that of most art styles, based on an in-depth study of image style transfer, this paper compared the effects of various style transfer models, and selected a Laplacian pyramid network to carry out a study of seismic section stylization. It transmits low-resolution global style patterns through a drafting network, revises high-resolution local details through correction networks, and aggregates all pyramid layers to output final stylized images of seismic profiles. Experiments show that this method can effectively convey the whole style pattern without losing the original image content. This style transfer method, based on the Laplacian pyramid network, provides theoretical guidance for the fast and objective application of the model to seismic data features.