Keywords
How to Cite
Abstract
The problem of noise removal and restoration from digital images is relevant in computer vision, medical diagnostics, artificial intelligence systems, and many other fields. The introduction of various noises into an image, such as salt-and-pepper or Gaussian noise, degrades the image quality and complicates its analysis. This article analyzes the process of image denoising and restoration based on mathematical modeling. In particular, the application of fuzzy set theory to this problem is discussed.
References
1. Xintao, D., Yonglong, L., Liping, S., and Fulong, C. [2014]. “Color Balloon Snakes for Face Segmentation,” Int’l J. for Light and Electron Optics, vol. 126, no. 11, pp. 2538–2542.
2. Feng, J., Cao, Z, and Pi, Y. [2013]. “Multiphase SAR Image Segmentation With Statistical-Model-Based Active Contours,” IEEE Trans. Geoscience and Remote Sensing, vol. 51, no. 7, pp. 4190 – 4199.
3. Thurley, J. M. and Danell, V. [2012]. “Fast Morphological Image Processing Open-Source Extensions for GPU Processing With CUDA,” IEEE J. Selected Topics in Signal Processing, vol. 6, no. 7, pp. 849–855.
4. Kaushik Roy, K., Bhattacharya, P., and Suen, C. Y. [2012]. “Iris Segmentation Using Game Theory,” Signal, Image and Video Processing, vol. 6, no. 2, pp. 301–315.
5. Krizhevsky, A., Sutskever, I., and Hinton, G. E. [2012]. “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems 25, NIPS 2012, pp. 1097–1105.
6. Beyerer, J., Puente Leon, F. and Frese, C. [2016]. Machine Vision-Automated Visual Inspection: Theory, Practice, and Applications, Springer-Verlag, Berlin, Germa New York.
This work is licensed under a Creative Commons Attribution 4.0 International License.