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Found 4 papers, 2 papers with code
Deep Image Debanding
Banding or false contour is an annoying visual artifact whose impact is even more pronounced in ultra high definition, high dynamic range, and wide colour gamut visual content, which is becoming increasingly popular.
Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge
This casts great challenges to deep neural network (DNN) based blind IQA (BIQA), which requires large-scale training data that is representative of the natural image distribution.
Quantifying Visual Image Quality: A Bayesian View
Image quality assessment (IQA) models aim to establish a quantitative relationship between visual images and their perceptual quality by human observers.
FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology
Out-of-focus microscopy lens in digital pathology is a critical bottleneck in high-throughput Whole Slide Image (WSI) scanning platforms, for which pixel-level automated Focus Quality Assessment (FQA) methods are highly desirable to help significantly accelerate the clinical workflows.
