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Miha Fuderer

dblp:181/7612 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video coding · 67% Multimedia systems and quality of experience · 33%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video coding › image quality assessment
artifact perception
0.212016
The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images · IEEE Trans. Image Process. 2016
Image and video coding
image quality assessment
0.212016
The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images · IEEE Trans. Image Process. 2016
Multimedia systems and quality of experience
subjective quality assessment
0.212016
The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images · IEEE Trans. Image Process. 2016
Medical and health informatics › medical imaging
magnetic resonance imaging
0.112016
The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images · IEEE Trans. Image Process. 2016
Medical and health informatics
medical imaging
0.112016
The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images · IEEE Trans. Image Process. 2016

Methods — techniques the papers use, named apart from their topics

subjective experiment · 0.5
YearPublicationVenuePosition
2016 The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images
abstract
Magnetic resonance (MR) imaging is vulnerable to a variety of artifacts, which potentially degrade the perceived quality of MR images and, consequently, may cause inefficient and/or inaccurate diagnosis. In general, these artifacts can be classified as structured or unstructured depending on the correlation of the artifact with the original content. In addition, the artifact can be white or colored depending on the flatness of the frequency spectrum of the artifact. In current MR imaging applications, design choices allow one type of artifact to be traded off with another type of artifact. Hence, to support these design choices, the relative impact of structured versus unstructured or colored versus white artifacts on perceived image quality needs to be known. To this end, we conducted two subjective experiments. Clinical application specialists rated the quality of MR images, distorted with different types of artifacts at various levels of degradation. The results demonstrate that unstructured artifacts deteriorate quality less than structured artifacts, while colored artifacts preserve quality better than white artifacts.
Hantao Liu, Jos Koonen, Miha Fuderer, Ingrid Heynderickx
IEEE Trans. Image Process.3