Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
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
SUPER-RESOLUTION USING MULTIPLE QUANTIZED IMAGES
Download
index.pdf
Date
2010-09-29
Author
Ozcelikkale, Ayca
Akar, Gözde
ÖZAKTAŞ, MEMDUH HALDUN
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
141
views
0
downloads
Cite This
In this paper, we study the effect of limited amplitude resolution (pixel depth) in super-resolution problem. The problem we address differs from the standard super-resolution problem in that amplitude resolution is considered as important as spatial resolution. We study the trade-off between the pixel depth and spatial resolution of low resolution (LR) images in order to obtain the best visual quality in the reconstructed high resolution (HR) image. The proposed framework reveals great flexibility in terms of pixel depth and number of LR images in super-resolution problem, and demonstrates that it is possible to obtain target visual qualities with different measurement scenarios including images with different amplitude and spatial resolutions.
Subject Keywords
Pixel depth
,
Amplitude resolution
,
Quantization
,
Super-resolution
URI
https://hdl.handle.net/11511/47462
DOI
https://doi.org/10.1109/icip.2010.5651039
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
SUPER-RESOLUTION RECONSTRUCTION OF HYPERSPECTRAL IMAGES VIA AN IMPROVED MAP-BASED APPROACH
Irmak, Hasan; Akar, Gözde; Yuksel, Seniha Esen; Aytaylan, Hakan (2016-07-15)
Super-resolution Reconstruction (SRR) is technique to increase the spatial resolution of images. It is especially useful for hyperspectral images (HSI), which have good spectral resolution but low spatial resolution. In this study, we propose an improvement to our previous work and present a novel MAP-MRF (maximum a posteriori-Markov random Fields) based approach for the SRR of HSI. The key point of our approach is to find the abundance maps of an HSI and perform SRR on the abundance maps using MRF based en...
LASP Local adaptive super pixels
İNCE, Kutalmış Gökalp; Çığla, Cevahir; Alatan, Abdullah Aydın (2015-09-30)
In this study, a novel gradient ascent approach is proposed for super-pixel extraction in which spectral statistics and super-pixel geometry are utilized to obtain an optimal Bayesian classifier for pixel to super-pixel label assignment. Utilization of the spectral variances and super-pixel areas reduces the dependency on user selected global parameters, while increasing robustness and adaptability. Proposed Local Adaptive Super-Pixels (LASP) approach exploits hexagonal tiling, while achieving some refineme...
A comparative evaluation of super – resolution methods on color images
Erbay, Fulya; Akar, Gözde; Department of Electrical and Electronics Engineering (2011)
In this thesis, it is proposed to get the high definition color images by using super – resolution algorithms. Resolution enhancement of RGB, HSV and YIQ color domain images is presented. In this study, three solution methods are presented to improve the resolution of HSV color domain images. These solution methods are suggested to beat the color artifacts on super resolution image and decrease the computational complexity in HSV domain applications. PSNR values are measured and compared with the results of...
Effect of Visual Context Information for Super Resolution Problems
Akar, Gözde; Aykut, Ekin; Cengiz, Baran; Bocek, Kadircan (2019-04-26)
In this study, the effect of visual context information to the performance of learning-based techniques for the super resolution problem is analyzed. Beside the interpretation of the experimental results in detail, its theoretical reasoning is also achieved in the paper. For the experiments, two different visual datasets composed of natural and remote sensing scenes are utilized. From the experimental results, we observe that keeping visual context information in the course of parameter learning for convolu...
Multi-modal stereo-vision using infrared / visible camera pairs
Yaman, Mustafa; Kalkan, Sinan; Department of Computer Engineering (2014)
In this thesis, a novel method for computing disparity maps from a multi-modal stereo-vision system composed of an infrared-visible camera pair is introduced. The method uses mutual information as the basic similarity measure where a segmentation based adaptive windowing mechanism is proposed along with a novel mutual information computation surface for greatly enhancing the results. Besides, the method incorporates joint prior probabilities when computing the cost matrix in addition to negative mutual info...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
A. Ozcelikkale, G. Akar, and M. H. ÖZAKTAŞ, “SUPER-RESOLUTION USING MULTIPLE QUANTIZED IMAGES,” 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47462.
