ADVANCEMENTS IN ARTIFICIAL INTELLIGENCE FOR IMAGE PROCESSING
Keywords:
Image processing has evolved from basic algorithms like edge detection and histogram equalization to sophisticatedAbstract
The integration of Artificial Intelligence (AI) into image processing has dramatically transformed the landscape of computer vision. This article offers an in-depth exploration of AI techniques, particularly Convolutional Neural Networks (CNNs) and other deep learning models, in advancing image processing capabilities. We dissect the methodologies, evaluate their performance through various metrics, analyze their applications, and discuss future research avenues to overcome current challenges and limitations.References
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
Dosovitskiy, A., & Brox, T. (2016). Inverting visual representations with convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(4), 735-745.
Dosovitskiy, A., & Springenberg, J. T. (2015). Discriminative unsupervised feature learning with Exemplar Convolutional Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(9), 1734-1747.
Dosovitskiy, A., & Shubina, M. (2017). Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 825-836.
Radford, A., Kim, J. W., Hallacy, C., et al. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).