Irmak, Hasan
Akar, Gözde
Yuksel, Seniha Esen
Aytaylan, Hakan
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 energy minimization, without needing any other additional source of information. In order to do so, first, PCA is used to determine the endmembers. Second, SISAL and fully constraint least squares (FCLS) are used to estimate the abundance maps. Third, in order to find the high resolution abundance maps, the ill-posed inverse SRR problem for abundances is regularized with a MAP-MRF based approach. The MAP-MRF formulation is restricted with the constraints which are specific to the abundances. Using the non-linear programming (NLP) techniques, the convex MAP formulation is minimized and High Resolution (HR) abundance maps are obtained. Then, these maps are used to construct the HR HSI. This improved SRR method is verified on real data sets, and quantitative performance comparison is achieved using PSNR, SSIM and PSNR metrics. Our results indicate that this improved method gives very close results to the original high resolution images, keeps the spectral consistency, and performs better than the compared algorithms.


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Çalışkan, Akın; Koz, Alper; Alatan, Abdullah Aydın (2015-07-31)
The high spectral resolution of hyperspectral images (HSI) requires a heavy processing load. Assigning each pixel to a group in the image, which is called superpixel, and processing the superpixels instead of the pixels is resorted as a means to overcome this challenge in the hyperspectral literature. In this paper, we propose an algorithm to segment a hyperspectral image into superpixels by means of iteratively updating the boundary pixels of superpixels. We first explore the optimal similarity metric for ...
Ozcelikkale, Ayca; Akar, Gözde; ÖZAKTAŞ, MEMDUH HALDUN (2010-09-29)
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...
Bayesian multi frame super resolution
Turgay, Emre; Akar, Gözde; Akar, Nail; Department of Electrical and Electronics Engineering (2014)
This thesis aims at increasing the effective resolution of an image using a set of low resolution images. This process is referred to as super resolution (SR) image reconstruction in the literature. This work proposes maximum a-posteriori (MAP) based iterative reconstruction methods for this problem. The first contribution of the thesis is a novel edge preserving SR image reconstruction method. The proposed MAP based estimator uses local gradient direction and amplitude for optimal noise reduction while prese...
Image fusion for improving spatial resolution of multispectral satellite images
Ünlüsoy, Deniz; Süzen, Mehmet Lütfi; Department of Geological Engineering (2013)
In this study, four different image fusion techniques have been applied to high spectral and low spatial resolution satellite images with high spatial and low spectral resolution images to obtain fused images with increased spatial resolution, while preserving spectral information as much as possible. These techniques are intensity-hue-saturation (IHS) transform, principle component analysis (PCA), Brovey transform (BT), and Wavelet transform (WT) image fusion. Images used in the study belong to Çankırı reg...
An Investigation on hyperspectral image classifiers for remote sensing
Özdemir, Okan Bilge; Çetin, Yasemin; Department of Information Systems (2013)
Hyperspectral image processing is improved by the capabilities of multispectral image processing with high spectral resolution. In this thesis, we explored hyperspectral classification with Support Vector Machines (SVM), Maximum Likelihood (ML) and KNearest Neighborhood algorithms. We analyzed the effect of training data on classification accuracy. For this purpose, we implemented three different training data selection methods; first N sample selection, randomly N sample selection and uniformly N sample se...
Citation Formats
H. Irmak, G. Akar, S. E. Yuksel, and H. Aytaylan, “SUPER-RESOLUTION RECONSTRUCTION OF HYPERSPECTRAL IMAGES VIA AN IMPROVED MAP-BASED APPROACH,” 2016, Accessed: 00, 2020. [Online]. Available: