Ozcelikkale, Ayca
Akar, Gözde
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.


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
A. Ozcelikkale, G. Akar, and M. H. ÖZAKTAŞ, “SUPER-RESOLUTION USING MULTIPLE QUANTIZED IMAGES,” 2010, Accessed: 00, 2020. [Online]. Available: