Image Quality
Image quality assessment (IQA) focuses on objectively measuring and improving the visual fidelity of images, crucial for various applications from medical imaging to autonomous driving. Current research emphasizes developing robust no-reference IQA methods, often employing deep learning architectures like transformers and convolutional neural networks, and exploring the use of generative AI models for image enhancement and compression. These advancements are significant because they enable automated quality control, improved diagnostic accuracy in healthcare, and more efficient data management across numerous fields.
Papers
PICNIQ: Pairwise Comparisons for Natural Image Quality Assessment
Nicolas Chahine, Sira Ferradans, Jean Ponce
Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
Paraskevas Pegios, Manxi Lin, Nina Weng, Morten Bo Søndergaard Svendsen, Zahra Bashir, Siavash Bigdeli, Anders Nymark Christensen, Martin Tolsgaard, Aasa Feragen
Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence
Marina Manso Jimeno, Keerthi Sravan Ravi, Maggie Fung, John Thomas Vaughan,, Sairam Geethanath
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
Manxi Lin, Jakob Ambsdorf, Emilie Pi Fogtmann Sejer, Zahra Bashir, Chun Kit Wong, Paraskevas Pegios, Alberto Raheli, Morten Bo Søndergaard Svendsen, Mads Nielsen, Martin Grønnebæk Tolsgaard, Anders Nymark Christensen, Aasa Feragen