Using full-reference image quality metrics for automatic image sharpening

2014 ◽  
Author(s):  
Lukáš Krasula ◽  
Karel Fliegel ◽  
Patrick Le Callet ◽  
Miloš Klíma
Keyword(s):  
Image Quality ◽  
Quality Metrics ◽  
Reference Image ◽  
Image Sharpening ◽  
Full Reference ◽  
Image Quality Metrics

Automatic Detection of Game Engine Artifacts Using Full Reference Image Quality Metrics

2017 ◽  
Vol 14 (3) ◽  
pp. 1-17 ◽  
Author(s):  
Rafał Piórkowski ◽  
Radosław Mantiuk ◽  
Adam Siekawa
Keyword(s):  
Image Quality ◽  
Automatic Detection ◽  
Quality Metrics ◽  
Reference Image ◽  
Game Engine ◽  
Full Reference ◽  
Image Quality Metrics

Full-Reference Image Quality Metrics Performance Evaluation Over Image Quality Databases

2013 ◽  
Vol 38 (9) ◽  
pp. 2327-2356 ◽  
Author(s):  
Atidel Lahoulou ◽  
Ahmed Bouridane ◽  
Emmanuel Viennet ◽  
Mourad Haddadi
Keyword(s):  
Performance Evaluation ◽  
Image Quality ◽  
Quality Metrics ◽  
Reference Image ◽  
Full Reference ◽  
Image Quality Metrics

Full reference image quality metrics and their performance

2011 ◽  
Author(s):  
Kuryati Kipli ◽  
Shankar Krishnan ◽  
Nurdiani Zamhari ◽  
Mohd Saufee Muhammad ◽  
Sh.Masniah Wan Masra ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Metrics ◽  
Reference Image ◽  
Full Reference ◽  
Image Quality Metrics

scholarly journals Evaluation of DISORDER: retrospective image motion correction for volumetric brain MRI in a pediatric setting

2020 ◽  
Author(s):  
Katy Vecchiato ◽  
Alexia Egloff ◽  
Olivia Carney ◽  
Ata Siddiqui ◽  
Emer Hughes ◽  
...  
Keyword(s):  
Image Quality ◽  
Motion Correction ◽  
Spin Echo ◽  
Brain Mri ◽  
Quality Metrics ◽  
Image Motion ◽  
Reference Image ◽  
Outlier Rejection ◽  
Image Quality Metrics ◽  
Correction Technique

Background and Purpose: Head motion causes image degradation in brain MRI examinations, negatively impacting image quality, especially in pediatric populations. Here, we used a retrospective motion correction technique in children and assessed image quality improvement for 3D MRI acquisitions. Material and Methods: We prospectively acquired brain MRI at 3T using 3D sequences, T1-weighted MPRAGE, T2-weighted Turbo Spin Echo and FLAIR, in 32 unsedated children, including 7 with epilepsy (age range 2-18 years). We implemented a novel motion correction technique: Distributed and Incoherent Sample Orders for Reconstruction Deblurring using Encoding Redundancy (DISORDER). For each subject and modality, we obtained 3 reconstructions: as acquired (Aq), after DISORDER motion correction (Di), and Di with additional outlier rejection (DiOut). We analyzed 288 images quantitatively, measuring 2 objective no-reference image quality metrics: Gradient Entropy (GE) and MPRAGE White Matter Homogeneity (WM-H). As a qualitative metric, we presented blinded and randomized images to 2 expert neuroradiologists who scored them for clinical readability. Results: Both image quality metrics improved after motion correction for all modalities and improvement correlated with the amount of intrascan motion. Neuroradiologists also considered the motion corrected images as of higher quality (Wilcoxon z=-3.164 MPRAGE, z=-2.066 TSE, z=-2.645 FLAIR, for all p<0.05). Conclusions: Retrospective image motion correction with DISORDER increased image quality both from an objective and qualitative perspective. In 75% of sessions, at least one sequence was improved by this approach, indicating the benefit of this technique in un-sedated children for both clinical and research environments.

Sign in / Sign up

Export Citation Format

Share Document