Image Quality Estimation
13 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Latest papers with no code
Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ), as well as the human perception of fingerprint quality on several public databases.
Impact of Face Image Quality Estimation on Presentation Attack Detection
In this paper, we study the effect of quality assessment methods on filtering bona fide and attack samples, their impact on PAD systems, and how the performance of such systems is improved when training on a filtered (by quality) dataset.
Can No-reference features help in Full-reference image quality estimation?
Recent works in Full-reference IQA research perform pixelwise comparison between deep features corresponding to query and reference images for quality prediction.
A Comparative Study of Fingerprint Image-Quality Estimation Methods
One of the open issues in fingerprint verification is the lack of robustness against image-quality degradation.
A Shift-insensitive Full Reference Image Quality Assessment Model Based on Quadratic Sum of Gradient Magnitude and LOG signals
In this paper, we propose an FR-IQA model with the quadratic sum of the GM and the LOG signals, which obtains good performance in image quality estimation considering shift-insensitive property for not well-registered reference and distortion image pairs.
Capturing Localized Image Artifacts through a CNN-based Hyper-image Representation
Thus some image-based small-data applications first train their framework on a collection of patches (instead of the entire image) to better learn the representation of localized artifacts.
Deep Multi-Patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation
We propose a deep multi-patch aggregation network training approach, which allows us to train models using multiple patches generated from one image.
SOM: Semantic Obviousness Metric for Image Quality Assessment
We propose to extract two types of features, one to measure the semantic obviousness of the image and the other to discover local characteristic.
Blind Image Quality Assessment using Semi-supervised Rectifier Networks
The biggest hurdles to these efforts are: 1) the difficulty of generalizing across diverse types of distortions and 2) collecting the enormity of human scored training data that is needed to learn the measure.
