Research on the Underlying Principles and Deep Learning Algorithms based on Image Style Conversion Techniques

Xiujiang Tan; Long Tan

doi:10.62051/2z2ngm94

Authors

Xiujiang Tan
Long Tan

DOI:

https://doi.org/10.62051/2z2ngm94

Keywords:

Image Style Transfer; Deep Learning; Convolutional Neural Networks; Generative Adversarial Networks; Content Representation.

Abstract

Image style transformation techniques are an important research area in the field of computer vision, aiming to combine the content of one image with the artistic style of another image to create new images with the content of the input image and the style of the artistic style image. The development of this field has benefited from the rapid development of deep learning algorithms, especially the application of techniques such as Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). At the heart of image style transformation is how to capture both the content and artistic style of the input image. This is typically achieved by using different levels of activation of the convolutional neural network as content and style representations. The content representation captures the object and structural information in the image, while the style representation captures the texture and colour information of the image. To capture the style of an image, a Gram matrix is typically used to measure the correlation between features at a particular level. This matrix representation allows us to capture texture information and colour distribution, thus enabling style migration. The goal of the image style transformation model is to minimise both content loss and style loss to produce synthetic images. Content loss typically uses Euclidean distance to measure the difference between content representations, while style loss involves the distance between Gram matrices. GANs have become an important tool in image style transformation. The generator network is responsible for generating synthetic images and the discriminator network evaluates the realism of the generated images. Through adversarial training, the generator and discriminator networks constantly compete to improve the quality of the generated images. Finally, this study highlights the potential applications of image style conversion techniques in the fields of art creation, image editing and virtual reality, and proposes directions for future research on deep learning algorithms and underlying principles to further improve the efficiency and quality of image style conversion techniques. The continuous development of this field will bring new opportunities for image processing and creative applications.

Downloads

Download data is not yet available.

References

Attallah, O., Aslan, M. F., & Sabanci, K. (2022). A framework for lung and colon cancer diagnosis via lightweight deep learning models and transformation methods. Diagnostics, 12(12), 2926.

Khalifa, N. E., Loey, M., & Mirjalili, S. (2022). A comprehensive survey of recent trends in deep learning for digital images augmentation. Artificial Intelligence Review, 1-27.

Zhang, L., Qi, Z., & Meng, F. (2022). A review on the construction of business intelligence system based on unstructured image data. Procedia Computer Science, 199, 392-398.

Wu, Y., Cheng, M., Huang, S., Pei, Z., Zuo, Y., Liu, J., ... & Shao, W. (2022). Recent advances of deep learning for computational histopathology: principles and applications. Cancers, 14(5), 1199.

Xu, M., Yoon, S., Fuentes, A., & Park, D. S. (2023). A comprehensive survey of image augmentation techniques for deep learning. Pattern Recognition, 109347.

Bai, B., Yang, X., Li, Y., Zhang, Y., Pillar, N., & Ozcan, A. (2023). Deep learning-enabled virtual histological staining of biological samples. Light: Science & Applications, 12(1), 57.

Saravi, B., Hassel, F., Ülkümen, S., Zink, A., Shavlokhova, V., Couillard-Despres, S., ... & Lang, G. M. (2022). Artificial intelligence-driven prediction modeling and decision making in spine surgery using hybrid machine learning models. Journal of Personalized Medicine, 12(4), 509.

Mumuni, A., & Mumuni, F. (2022). Data augmentation: A comprehensive survey of modern approaches. Array, 100258.

Ozyoruk, K. B., Can, S., Darbaz, B., Başak, K., Demir, D., Gokceler, G. I., ... & Turan, M. (2022). A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded. Nature Biomedical Engineering, 6(12), 1407-1419.

Li, Z., Wang, Y., Zhang, N., Zhang, Y., Zhao, Z., Xu, D., ... & Gao, Y. (2022). Deep learning-based object detection techniques for remote sensing images: A survey. Remote Sensing, 14(10), 2385.

Mao, J., Zhu, Y., Chen, M., Chen, G., Yan, C., & Liu, D. (2023). A contradiction solving method for complex product conceptual design based on deep learning and technological evolution patterns. Advanced Engineering Informatics, 55, 101825.

Shen, S. C. Y., & Buehler, M. J. (2022). Nature-inspired architected materials using unsupervised deep learning. Communications Engineering, 1(1), 37.

Wang, J., Wang, C., Lin, Q., Luo, C., Wu, C., & Li, J. (2022). Adversarial attacks and defenses in deep learning for image recognition: A survey. Neurocomputing.

Yang, Y. (2022). Application of LSTM Neural Network Technology Embedded in English Intelligent Translation. Computational Intelligence and Neuroscience, 2022.

Hyun, Y., & Kim, D. (2022). Development of deep-learning-based single-molecule localization image analysis. International Journal of Molecular Sciences, 23(13), 6896.

Garg, M., Ubhi, J. S., & Aggarwal, A. K. (2023). Neural style transfer for image steganography and destylization with supervised image to image translation. Multimedia Tools and Applications, 82(4), 6271-6288.

Liang, P. P., Zadeh, A., & Morency, L. P. (2022). Foundations and recent trends in multimodal machine learning: Principles, challenges, and open questions. arXiv preprint arXiv:2209.03430.

Fan, Z., Shi, L., Xi, C., Wang, H., Wang, S., & Wu, G. (2022). Real time power equipment meter recognition based on deep learning. IEEE Transactions on Instrumentation and Measurement, 71, 1-15.

Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023). A review of deep learning techniques for speech processing. Information Fusion, 101869.

Wang, Y., Huang, K., Fang, J., Yan, M., Wu, E., & Zeng, H. (2023). Mid-infrared single-pixel imaging at the single-photon level. Nature Communications, 14(1), 1073.

Alsaif, H., Guesmi, R., Alshammari, B. M., Hamrouni, T., Guesmi, T., Alzamil, A., & Belguesmi, L. (2022). A novel data augmentation-based brain tumor detection using convolutional neural network. Applied Sciences, 12(8), 3773.

Nomani, A., Ansari, Y., Nasirpour, M. H., Masoumian, A., Pour, E. S., & Valizadeh, A. (2022). PSOWNNs-CNN: a computational radiology for breast cancer diagnosis improvement based on image processing using machine learning methods. Computational Intelligence and Neuroscience, 2022.

Huang, L., Luo, R., Liu, X., & Hao, X. (2022). Spectral imaging with deep learning. Light: Science & Applications, 11 (1), 61.

Zhou, H., Dong, J., Cheng, J., Dong, W., Huang, C., Shen, Y., ... & Zhang, X. (2022). Photonic matrix multiplication lights up photonic accelerator and beyond. Light: Science & Applications, 11(1), 30.

Wang, Z., Sun, Z., Yin, H., Liu, X., Wang, J., Zhao, H., ... & Yu, X. F. (2022). Data‐Driven Materials Innovation and Applications. Advanced Materials, 34(36), 2104113.

Santini, F., Wasserthal, J., Agosti, A., Deligianni, X., Keene, K. R., Kan, H. E., ... & Pichiecchio, A. (2023). Deep Anatomical Federated Network (Dafne): an open client/server framework for the continuous collaborative improvement of deep-learning-based medical image segmentation. arXiv preprint arXiv:2302.06352.

Pandian, J. A., Kumar, V. D., Geman, O., Hnatiuc, M., Arif, M., & Kanchanadevi, K. (2022). Plant disease detection using deep convolutional neural network. Applied Sciences, 12(14), 6982.

Goswami, A., Sharma, D., Mathuku, H., Gangadharan, S. M. P., Yadav, C. S., Sahu, S. K., ... & Imran, H. (2022). Change detection in remote sensing image data comparing algebraic and machine learning methods. Electronics, 11 (3), 431.

Moshayedi, A. J., Roy, A. S., Kolahdooz, A., & Shuxin, Y. (2022). Deep learning application pros and cons over algorithm deep learning application pros and cons over algorithm. EAI Endorsed Transactions on AI and Robotics, 1(1).