Image Enhacement and Fusion with the Random Neural Network
Enhancing image quality and combining observations into a coherent description are essential tools in various image processing applications such as multimedia publishing, target recognition, and medical imaging. In this paper we propose two novel approaches for image enlargement and image fusion using the Random Neural Network (RNN) model [1,2,3,4], whic has already been successfully applied to the problems such as still and moving image compression , and image segmentation . The advantage of the RNN model is that is closer to biophysical reality and mathematically more tractable than standard neural methods, especially when used as a recurrent structure.
BAKIRCIOĞLU, Hakan; GELENBE, Erol; and KOÇAK, Taşkın (1997) "Image Enhacement and Fusion with the Random Neural Network," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 5: No. 1, Article 4. Available at: https://journals.tubitak.gov.tr/elektrik/vol5/iss1/4