Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Image compression based on a fractal theory of iterated function systems
Download
038641.pdf
Date
1995
Author
Motlagh, Reza H
Metadata
Show full item record
Item Usage Stats
10
views
0
downloads
Cite This
URI
https://hdl.handle.net/11511/10996
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Image compression using random neural networks
Sungur, M (1998-10-28)
Random neural network is a novel pulsed neural network model which has nice analytical features. In this paper, we review the use of the random neural network for the lossy compression of digital gray level images.
Image data compression using counterpropagation network.
Erham, Hakan; Department of Computer Engineering (1992)
Image compression by using wavelet transform
Yıldırım, Mert Funda; Yücel, Melek D.; Department of Electrical and Electronics Engineering (1999)
Image data compression using adaptive transform coding techniques
Öcal, S. Orhan; Yücel, Melek D.; Department of Electrical and Electronics Engineering (1991)
Image generation by back-propagation on input using a discriminator network
Taplı, Merve; Akbaş, Emre; Department of Computer Engineering (2021-9-08)
In this thesis, we propose an image generation method that only involves a discriminator network; no generator or decoder networks are required. To generate an image, we iteratively apply an adversarial attack on the discriminator by updating the input image, which is noise at the beginning, to maximize the discriminator's output score. Generated images are then used as negative examples, together with the real images as positive examples, to fine-tune the discriminator. After several rounds of generation a...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
R. H. Motlagh, “Image compression based on a fractal theory of iterated function systems,” Middle East Technical University, 1995.