A Robust quality metric for image super resolution /

Download
2015
Kipman, Yiğit
Superresolution have become an active topic in image processing in the last decade. Various superresolution algorithms have been developed; however these superresolution algorithms may introduce defects such as blurring, aliasing, added noise and ringing. Evaluating the performance of these superresolution algorithms is an important problem; because the original high resolution image is not available while quantifying the quality of superresolution image. Subjective tests can be made to quantify the perceived image quality; but they are time-consuming and expensive. Only a few objective quality ssessment algorithms are proposed that evaluate the quality of superresoluted image from its low-resolution (LR) pair; but these do not correlate well with the subjective tests. In this thesis, a quality assessment algorithm for image superresolution that follows the philosophy of natural scene statistics (NSS) is analyzed and an improvement is proposed. A statistical model of frequency energy falloff characteristics of high resolution (HR) images is developed and a quality measure is calculated from the departures from HR image statistics. A no-reference spatial image quality assesment measure that also follows the philosophy of NSS is incorporated in the proposed algorithm to improve the robustness of the metric against noise. It is shown that the proposed approach is robust against noise and correlates well with the human visual system.

Suggestions

Fast 3D reconstruction from medical image series based on thresholding method Eşikleme metodunu kullanarak medikal görüntü serisinden hizli 3 boyutlu model oluşturma
Öz, Sinan; Serinağaoğlu Doğrusöz, Yeşim (2010-07-15)
Many practical applications in the field of medical image processing need valid, reliable and fast image segmentation. In this study, we propose a semi-automatic segmentation approach. In this approach, an extended version of the Otsu's method for three level thresholding and a recursive connected component algorithm are combined. The segmentation process is accomplished using Extended Otsu's method and labeling in each consecutive slice. Extended Otsu's method is a thresholding method selecting two thresho...
A Shadow based trainable method for building detection in satellite images
Dikmen, Mehmet; Halıcı, Uğur; Department of Geodetic and Geographical Information Technologies (2014)
The purpose of this thesis is to develop a supervised building detection and extraction algorithm with a shadow based learning method for high-resolution satellite images. First, shadow segments are identified on an over-segmented image, and then neighboring shadow segments are merged by assuming that they are cast by a single building. Next, these shadow regions are used to detect the candidate regions where buildings most likely occur. Together with this information, distance to shadows towards illuminati...
A real-time, low-latency, FPGA implementation of the two dimensional discrete wavelet transform
Benderli, Oğuz; Tekmen, Yusuf Çağatay; Department of Electrical and Electronics Engineering (2003)
This thesis presents an architecture and an FPGA implementation of the two dimensional discrete wavelet transformation (DWT) for applications where row-based raw image data is streamed in at high bandwidths and local buffering of the entire image is not feasible. The architecture is especially suited for multi-spectral imager systems, such as on board an imaging satellite, however can be used in any application where time to next image constraints require real-time processing of multiple images. The latency...
Design and implementation of a novel visual analysis system for image clasiffication
Altintakan, Ümit Lütfü; Yazıcı, Adnan; Körpeoğlu, İbrahim; Department of Computer Engineering (2013)
Possibilities offered by the technology to create, share and disseminate image and video data have resulted in a rapid increase in the available visual data. However, the data is useless unless it is effectively accessed, which necessitates the semantic analysis of visual data. In this dissertation, we present a novel visual analysis system along with its application to image classification problem. We aim to address the challenges in the area originated from the semantic gap, and to facilitate the research...
Fusion of image segmentation with domain specific information under an unsupervised markov random fields model
Karadağ, Özge Öztimur; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2014)
The formulation of image segmentation problem is evolved considerably, from the early years of computer vision in 1970s to these years, in 2010s. While the initial studies offer mostly unsupervised approaches, a great deal of recent studies shift towards the supervised solutions. This is due to the advancements in the cognitive science and its influence on the computer vision research. Also, accelerated availability of computational power enables the researchers to develop complex algorithms. Despite the gr...
Citation Formats
Y. Kipman, “A Robust quality metric for image super resolution /,” M.S. - Master of Science, Middle East Technical University, 2015.