| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Show document Help

Title:Odstranjevanje pegastega šuma iz slik SAR z uporabo globokega učenja : magistrsko delo
Authors:ID Habjanič, Tadej (Author)
ID Gleich, Dušan (Mentor) More about this mentor... New window
Files:.pdf MAG_Habjanic_Tadej_2024.pdf (12,83 MB)
MD5: 967906D700A47FFEBC33C7638B436F3A
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Postopek odstranjevanja pegastega šuma je neizogiben pri obdelavi slik z radarjem s sintetično odprtino (SAR). Obstaja več različnih metod za odstranjevanje pegastega šuma, vendar se je postopek s konvolucijsko nevronsko mrežo (CNN) izkazal kot zelo učinkovita metoda. Pri preprosti strukturi CNN se še vedno izgubi precejšnje število podrobnosti na sliki. Za rešitev tega problema je bila uporabljena arhitektura kodirnika – dekoderja. Model se uči s pristopom, ki temelji na veliki količini podatkov, z uporabo algoritma gradientnega spuščanja s kombinacijo spreminjanja ojačanja pri odstranjevanju šuma in funkcije izgube celotne variacije. Poskusi, izvedeni na realnih slikah, kažejo, da ta metoda dosega pomembne izboljšave v primerjavi z ostalimi metodami.
Keywords:pegasti šum, radar s sintetično odprtino, konvolucijska nevronska mreža, arhitektura kodirnik – dekodirnik
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[T. Habjanič]
Year of publishing:2023
Number of pages:1 spletni vir (1 datoteka PDF (X, 62 f.))
PID:20.500.12556/DKUM-86581-80416078-64b0-d96a-cdd0-8725e26234cd New window
UDC:004.85:[004.932:528.8.044.2](043.2)
COBISS.SI-ID:189928707 New window
Publication date in DKUM:06.02.2024
Views:274
Downloads:37
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
Copy citation
  
Average score:(0 votes)
Your score:Voting is allowed only for logged in users.
Share:Bookmark and Share


Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:18.12.2023

Secondary language

Language:English
Title:Despeckling of SAR image using deep learning
Abstract:Despecklink process is inevitable in synthetic aperture radar (SAR) images processing. There are several different methods for removing speckled noise, but the Convolutional Neural Network (CNN) approach has proven to be a very effective method. With a simple CNN structure, a considerable amount of detail in the image is still lost. An encoder – decoder architecture was used to solve this problem. The model is trained using an approach based on a large amount of data, utilizing the gradient descent algorithm in combination with adaptive gain tuning for speckle removal and the total variation loss function. Experiments performed on real images show that this method achieves significant improvements compared to other methods.
Keywords:descpeckling, synthetic aperture radar, convolutional neural network, encoder – decoder architecture


Comments

Leave comment

You must log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica