Image Style Transfer Research Articles

Research on the Principles and Applications of Generative Adversarial Networks in Image Style Transfer

Abstract. With the rapid development of machine learning, the field of image style transfer has received widespread attention, and scientists have gradually realized that this is an important field in computer vision. Image style transfer refers to the conversion of an image from one visual style to another and preserving the content. The technology of changing image style has a wide range of applications in various fields from art to virtual reality. Although current learning models have achieved some results in the study of image style transfer, they still face challenges in improving resolution and complex style transfer. This paper mainly introduces the background and characteristics of existing learning models that can achieve image style transfer. It introduces the principles, characteristics and limitations of GANs. In the basis of introduction, this paper also analyses the differences and different characteristics of GANs and summarizes the advantages and disadvantages of GANs. At the same time, this paper predicts future research directions, continuing to optimize these models, reducing training costs, and exploring hybrid methods to further advance image style transfer technology. These developments not only strengthen the theoretical research of image style transfer and GANs, but also have broad application potential in fields such as art, design and medical imaging.

Research on Image Style Transfer Based on a Generative Model

The generation of artificial intelligence technology is rapidly developing. As a representative, Sora created in text, image rendering, and video production shows excellent ability. This article discusses the image-style transmission algorithm based on generating a confrontation network (GAN) and compares it with the Sora model. Studies have shown that GAN performs well in image style transfer, especially in maintaining the consistency of the image content and generating high-quality images. In the future, the expected artificial intelligence technology will further improve the quality and diversity of generating content by combining the diffusion model and GAN and expanding its application potential in the fields of art creation and virtual reality.

Automated tree crown labeling with 3D radiative transfer modelling achieves human comparable performances for tree segmentation in semi-arid landscapes

Mapping tree crowns in arid or semi-arid areas, which cover around one-third of the Earth’s land surface, is a key methodology towards sustainable management of trees. Recent advances in deep learning have shown promising results for tree crown segmentation. However, a large amount of manually labeled data is still required. We here propose a novel method to delineate tree crowns from high resolution satellite imagery using deep learning trained with automatically generated labels from 3D radiative transfer modeling, intending to reduce human annotation significantly. The methodological steps consist of 1) simulating images with a 3D radiative transfer model, 2) image style transfer learning based on generative adversarial network (GAN) and 3) tree crown segmentation using U-net segmentation model. The delineation performances of the proposed method have been evaluated on a manually annotated dataset consisting of more than 40,000 tree crowns. Our approach, which relies solely on synthetic images, demonstrates high segmentation accuracy, with an F1 score exceeding 0.77 and an Intersection over Union (IoU) above 0.64. Particularly, it achieves impressive accuracy in extracting crown areas (r2 greater than 0.87) and crown densities (r2 greater than 0.72), comparable to that of a trained dataset with human annotations only. In this study, we demonstrated that the integration of a 3D radiative transfer model and GANs for automatically generating training labels can achieve performances comparable to human labeling, and can significantly reduce the time needed for manual labeling in remote sensing segmentation applications.

ST2SI: Image Style Transfer via Vision Transformer using Spatial Interaction

While retaining the original content structure, image style transfer uses style image to render it to obtain stylized images with artistic features. Because the content image contains different detail units and the style image has various style patterns, it is easy to cause the distortion of the stylized image. We proposes a new Style Transfer based on Vision Transformer using Spatial Interaction (ST2SI), which takes advantage of Spatial Interactive Convolution (SIC) and Spatial Unit Attention (SUA) to further enhance the content and style representation, so that the encoder can not only better learn the features of the content domain and the style domain, but also maintain the structural integrity of the image content and the effective integration of style features. Concretely, the high-order spatial interaction ability of Spatial Interactive Convolution can capture complex style patterns, and Spatial Unit Attention can balance the content information of different detail units through the change of attention weight, thus solving the problem of image distortion. Comprehensive qualitative and quantitative experiments prove the efficacy of our approach.

HDMA-CGAN: Advancing Image Style Transfer with Deep Learning

The integration of artificial intelligence (AI) and deep learning heralds a transformative era in pattern recognition and computer vision, notably in image style transfer. We introduce the hierarchical dynamic multi-attention cycle generative adversarial network (HDMA-CGAN), an innovative deep learning architecture poised to redefine image style transfer capabilities. HDMA-CGAN employs a novel multi-attention mechanism and color optimization strategies, enabling precise style replication with improved fidelity and vibrancy. Our model surpasses existing benchmarks in image quality, validated by leading metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID). Although HDMA-CGAN advances the state of the art, it necessitates high computational resources and faces challenges with very high-resolution images. Future work could explore optimizing the model’s efficiency for real-time applications and extending its application to video content. This work enhances the tools available for visual content creation and digital media enhancement, leveraging advanced pattern recognition and AI techniques to significantly impact computer vision and image processing.

Research on the Expanded Night Road Condition Dataset Based on the Improved CycleGAN

Abstract Image style transfer is a major area of study in image processing and has applications in creative production, special effects for film and television, and other areas. Image style transfer is the process of using style transfer technology to change a common image into one with a different style without changing the content. Image style transfer methods are mainly divided into traditional image style transfer methods and deep learning image style transfer methods. The two primary classifications of picture style transfer techniques are deep learning technologies and conventional methods. Traditional image style transfer methods have poor results and are difficult to apply in people's lives. With the quick advancements in machine learning, digital image processing, and computer vision, deep learning image style transfer methods have received widespread attention from researchers. Most of these methods use convolutional neural networks to achieve image style transfer on the premise of paired data sets, but obtaining paired data sets is difficult and costly. Accordingly, it is of great significance to study unpaired images to implement style transfer algorithms. The primary focus of this study is the CycleGAN network-based picture style transfer technique, and improves this algorithm in content compiler, style compiler. It is applied to the generation of night road conditions during autonomous driving training.

Research and design of image style transfer technology based on multi‐scale convolutional neural network feature fusion

AbstractTo reduce the occurrence of information loss and distortion in image style transfer, a method is proposed for researching and designing image style transfer technology based on multi‐scale convolutional neural networks (CNNs) feature fusion. Initially, the VGG19 model is designed for coarse and fine‐scale networks to achieve multi‐scale CNN feature extraction of target image information. Subsequently, while setting the corresponding feature loss function, an additional least‐squares penalty parameter is introduced to balance the optimal total loss function. Finally, leveraging the characteristics of stochastic gradient descent iteration, image features are fused and reconstructed to obtain better style transfer images. Experimental evaluations utilize peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), information entropy (IE), and mean squared error (MSE) as metrics for assessing the transferred images, comparing them with three typical image style transfer methods. Results demonstrate that the proposed method achieves optimal performance across all metrics, realizing superior image style transfer effects.

Exploring the Temporal Consistency of Arbitrary Style Transfer: A Channelwise Perspective.

Arbitrary image stylization by neural networks has become a popular topic, and video stylization is attracting more attention as an extension of image stylization. However, when image stylization methods are applied to videos, unsatisfactory results that suffer from severe flickering effects appear. In this article, we conducted a detailed and comprehensive analysis of the cause of such flickering effects. Systematic comparisons among typical neural style transfer approaches show that the feature migration modules for state-of-the-art (SOTA) learning systems are ill-conditioned and could lead to a channelwise misalignment between the input content representations and the generated frames. Unlike traditional methods that relieve the misalignment via additional optical flow constraints or regularization modules, we focus on keeping the temporal consistency by aligning each output frame with the input frame. To this end, we propose a simple yet efficient multichannel correlation network (MCCNet), to ensure that output frames are directly aligned with inputs in the hidden feature space while maintaining the desired style patterns. An inner channel similarity loss is adopted to eliminate side effects caused by the absence of nonlinear operations such as softmax for strict alignment. Furthermore, to improve the performance of MCCNet under complex light conditions, we introduce an illumination loss during training. Qualitative and quantitative evaluations demonstrate that MCCNet performs well in arbitrary video and image style transfer tasks. Code is available at https://github.com/kongxiuxiu/MCCNetV2.

Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions.

This paper proposes a multimodal Transformer model that uses time-series data to detect and predict winter road surface conditions. For detecting or predicting road surface conditions, the previous approach focuses on the cooperative use of multiple modalities as inputs, e.g., images captured by fixed-point cameras (road surface images) and auxiliary data related to road surface conditions under simple modality integration. Although such an approach achieves performance improvement compared to the method using only images or auxiliary data, there is a demand for further consideration of the way to integrate heterogeneous modalities. The proposed method realizes a more effective modality integration using a cross-attention mechanism and time-series processing. Concretely, when integrating multiple modalities, feature compensation through mutual complementation between modalities is realized through a feature integration technique based on a cross-attention mechanism, and the representational ability of the integrated features is enhanced. In addition, by introducing time-series processing for the input data across several timesteps, it is possible to consider the temporal changes in the road surface conditions. Experiments are conducted for both detection and prediction tasks using data corresponding to the current winter condition and data corresponding to a few hours after the current winter condition, respectively. The experimental results verify the effectiveness of the proposed method for both tasks. In addition to the construction of the classification model for winter road surface conditions, we first attempt to visualize the classification results, especially the prediction results, through the image style transfer model as supplemental extended experiments on image generation at the end of the paper.

Unsupervised Scene Image Text Segmentation Based on Improved CycleGAN

Scene image text segmentation is an important task in computer vision, but the complexity and diversity of backgrounds make it challenging. All supervised image segmentation tasks require paired semantic label data to ensure the accuracy of segmentation, but semantic labels are often difficult to obtain. To solve this problem, we propose an unsupervised scene image text segmentation model based on the image style transfer model cyclic uniform Generation Adversarial network (CycleGAN), which is trained by partial unpaired label data. Text segmentation is achieved by converting a complex background to a simple background. Since the images generated by CycleGAN cannot retain the details of the text content, we also introduced the Atrous spatial Pyramid pool module (ASPP) to obtain the features of the text from multiple scales. The resulting image quality is improved. The proposed method is verified by experiments on a synthetic data set, the IIIT 5k word data set and the MACT data set, which effectively segments the text and preserves the details of the text content.

A STUDY ON THE APPLICATION OF GEN AI TOOLS SUPPORTS TO FINANCIAL SERVICES

The Generative AI, also known as GenAI. It is a subset of artificial intelligence (AI) that is reshaping the collaboration with technology by the minute. GenAI possesses the unique ability to create the information. GenAI has been used for many applications across various areas, demonstrating its powerful capabilities for generating creative and even life-like content. As an innovation in GenAI image style transfer tools such as DeepArt and DeepDream have signifies the potential of generative AI tools by making users capable of creating amazing artworks out of ordinary images. Latest, the Generative Pre-trained Transformer (GPT) series, particularly ChatGPT-3 has taken the world by an electrifying storm for its remarkable ability to generate human-like text from simple prompts, igniting global imagination about the creative potential of AI. Gen AI tools uses in financial services for fraud detection, cybersecurity, Chabot’s, risk management, Alpha sense, and sentiment analysis. This research paper describes GenAI tools supports to financial services and some specific applications in both the financial and non-financial sector and also this research paper will examine the difficulties that lie ahead and the business opportunities for this fundamental technology that is going to transform our digital world. Also by addressing both the opportunities and challenges, this paper aims to provide a holistic perspective on the role of GenAI in the evolving Financial services. Keywords: GenAI, Tools, Financial and non-financial sector, Technology

Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1

Abstract. Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. By transporting significant amounts of latent energy towards the poles, they are crucial to maintaining the climate system's energy balance. Since there is no first-principle definition of an AR grounded in geophysical fluid mechanics, AR identification is currently performed by a multitude of expert-defined, threshold-based algorithms. The variety of AR detection algorithms has introduced uncertainty into the study of ARs, and the thresholds of the algorithms may not generalize to new climate datasets and resolutions. We train convolutional neural networks (CNNs) to detect ARs while representing this uncertainty; we name these models ARCNNs. To detect ARs without requiring new labeled data and labor-intensive AR detection campaigns, we present a semi-supervised learning framework based on image style transfer. This framework generalizes ARCNNs across climate datasets and input fields. Using idealized and realistic numerical models, together with observations, we assess the performance of the ARCNNs. We test the ARCNNs in an idealized simulation of a shallow-water fluid in which nearly all the tracer transport can be attributed to AR-like filamentary structures. In reanalysis and a high-resolution climate model, we use ARCNNs to calculate the contribution of ARs to meridional latent heat transport, and we demonstrate that this quantity varies considerably due to AR detection uncertainty.

TSGAN: A two-stage interpretable learning method for image cartoonization

Interpreting style transfer methods and generating high-quality style images are two challenging computer vision tasks. However, most of the current image style transfer methods are inexplicable, and their image cartoonilation performance is also not satisfactory due to the complex lines and rich abstract features of cartoon style. To alleviate these two issues, in this paper we propose a novel two-stage interpretable learning method, the two-stage generative adversarial network (TSGAN), for image cartoonization. Particularly, we first divide the generative model into a content learning stage and a stylization stage. The advantages are twofold. The first is that the finely differentiated two-stage image generation model has better interpretability and easy understanding. The second is that TSGAN can adjust the content and style details of the generated image. We further propose a Cartoon Image Enhance (CIE) module for dynamically sampling salient cartoon texture details from training data to generate cartoon images with higher quality. Experimental results show that our TSGAN is effective when compared with four representative methods in terms of visual, qualitative, and quantitative comparisons and user research.

Exploration of hyperparameter efficiency for image style transfer

Image style transfer is a popular computer vision technique that aims to merge the content of one image with the style of another to generate a unique, original image with a different aesthetic feel. Numerous models have been developed for various applications in this field, including portrait painting, art creation, and medical image processing, where additional information or annotations could be added to medical images, making them easier to read and understand. This study focuses on optimizing parameters within the pre-trained Visual Geometry Group (VGG19) network architecture, building on Google Brains 2017 work on Arbitrary style transfer in one model. The goal is to improve the quality and realism of the generated images by exploring different parameter combinations and fine-tuning weights and learning rates. This work carefully selects a range of styles and content to compare their effects during the optimization process. Finally, this fine-tuning process strikes a balance between content loss and style loss, which results in high-quality and more realistic images.

Style transfer for converting images into Chinese landscape painting based on CycleGAN

Chinese landscape painting is a prevalent and unique mode of artistic expression within traditional Chinese art. It boasts intricate techniques and demands a relatively high level of artistic skill. Recent advancements in artificial intelligence have ushered in the era of image style transfer techniques, making it feasible to convert landscape photographs into stunning Chinese landscape paintings. In this study, the author has developed an image translation model that enables mutual transformation between images of landscapes and Chinese landscape paintings. This technology significantly reduces the difficulty of creating Chinese landscape paintings, allowing more ordinary people in China to experience and appreciate the joy brought by traditional Chinese art. Experimental results indicate that the style transfer model based on CycleGAN has achieved significant success in this scenario. The generated artworks successfully integrate the style of Chinese landscape painting into the original images without altering the original composition and details. As a result, the original photos gain a certain level of artistic value. Additionally, this study innovatively explores the goal of reverse restoration of Chinese landscape paintings into images, highlighting both the similarities and differences with the current research, thus laying the foundation for future studies.

Music Style Transfer with Time-Varying Inversion of Diffusion Models

With the development of diffusion models, text-guided image style transfer has demonstrated great controllable and high-quality results. However, the utilization of text for diverse music style transfer poses significant challenges, primarily due to the limited availability of matched audio-text datasets. Music, being an abstract and complex art form, exhibits variations and intricacies even within the same genre, thereby making accurate textual descriptions challenging. This paper presents a music style transfer approach that effectively captures musical attributes using minimal data. We introduce a novel time-varying textual inversion module to precisely capture mel-spectrogram features at different levels. During inference, we utilize a bias-reduced stylization technique to get stable results. Experimental results demonstrate that our method can transfer the style of specific instruments, as well as incorporate natural sounds to compose melodies. Samples and code are available at https://lsfhuihuiff.github.io/MusicTI/.

Mean Teacher DETR with Masked Feature Alignment: A Robust Domain Adaptive Detection Transformer Framework

Unsupervised domain adaptation object detection(UDAOD) research on Detection Transformer(DETR) mainly focuses on feature alignment and existing methods can be divided into two kinds, each of which has its unresolved issues. One-stage feature alignment methods can easily lead to performance fluctuation and training stagnation. Two-stage feature alignment method based on mean teacher comprises a pretraining stage followed by a self-training stage, each facing problems in obtaining reliable pretrained model and achieving consistent performance gains. Methods mentioned above have not yet explore how to utilize the third related domain such as target-like domain to assist adaptation. To address these issues, we propose a two-stage framework named MTM, i.e. Mean Teacher-DETR with Masked Feature Alignment. In the pretraining stage, we utilize labeled target-like images produced by image style transfer to avoid performance fluctuation. In the self-training stage, we leverage unlabeled target images by pseudo labels based on mean teacher and propose a module called Object Queries Knowledge Transfer(OQKT) to ensure consistent performance gains of the student model. Most importantly, we propose masked feature alignment methods including Masked Domain Query-based Feature Alignment(MDQFA) and Masked Token-wise Feature Alignment(MTWFA) to alleviate domain shift in a more robust way, which not only prevent training stagnation and lead to a robust pretrained model in the pretraining stage, but also enhance the model's target performance in the self-training stage. Experiments on three challenging scenarios and a theoretical analysis verify the effectiveness of MTM.

Visual resource extraction and artistic communication model design based on improved CycleGAN algorithm.

Through the application of computer vision and deep learning methodologies, real-time style transfer of images becomes achievable. This process involves the fusion of diverse artistic elements into a single image, resulting in the creation of innovative pieces of art. This article centers its focus on image style transfer within the realm of art education and introduces an ATT-CycleGAN model enriched with an attention mechanism to enhance the quality and precision of style conversion. The framework enhances the generators within CycleGAN. At first, images undergo encoder downsampling before entering the intermediate transformation model. In this intermediate transformation model, feature maps are acquired through four encoding residual blocks, which are subsequently input into an attention module. Channel attention is incorporated through multi-weight optimization achieved via global max-pooling and global average-pooling techniques. During the model's training process, transfer learning techniques are employed to improve model parameter initialization, enhancing training efficiency. Experimental results demonstrate the superior performance of the proposed model in image style transfer across various categories. In comparison to the traditional CycleGAN model, it exhibits a notable increase in structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) metrics. Specifically, on the Places365 and selfi2anime datasets, compared with the traditional CycleGAN model, SSIM is increased by 3.19% and 1.31% respectively, and PSNR is increased by 10.16% and 5.02% respectively. These findings provide valuable algorithmic support and crucial references for future research in the fields of art education, image segmentation, and style transfer.

Combining CycleGAN and perceptual loss for image style transfer: Double training process

Combining CycleGAN and perceptual loss for image style transfer: Double training process

Arbitrary 3D stylization of radiance fields

3D Stylization that creates stylized multi-view images is quite challenging, as it requires not only generating images which align with the desired style but also maintaining consistency across different perspectives. Most previous image style transfer methods focus on the 2D image domain and stylize each view independently, suffering from multi-view inconsistency. To tackle this challenging problem, we build on the neural radiance fields (NeRF) to stylize each 3D scene, as NeRF inherently ensures consistency across multiple perspectives, and has two sub-networks of geometry and appearance where appearance stylization cannot change the geometry. To enable arbitrary style transfer and more explicit and precise style adjustment, we introduce the CLIP model, which allows for style transfer based on either a text prompt or an arbitrary style image. We employ an ensemble of loss functions, of which CLIP loss ensures the similarity between the shared latent embeddings and generated style images, and Mask Loss is to constrain the 3D geometry to avoid non-smooth surface of NeRF. Experimental results demonstrate the effectiveness of our arbitrary 3D stylization generalized across diverse datasets. The proposed method outperforms most image-based and text-based 3D stylization models in terms of style transfer quality, producing pleasing images.