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Found 10 papers, 4 papers with code
Exploring Deep Learning Image Super-Resolution for Iris Recognition
In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem.
Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images
For Sentinel-2, spectral information fusion allows for enhancing the 20 m and 60 m bands to the 10 m resolution.
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks
To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: A) generate and train a standard SR network on synthetic low-resolution - high-resolution (LR - HR) pairs or B) attempt to predict the degradations an LR image has suffered and use these to inform a customised SR network.
Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion
Current research in iris recognition is moving towards enabling more relaxed acquisition conditions.
Face2Text revisited: Improved data set and baseline results
To encourage the development of more human-focused descriptions, we developed a new data set of facial descriptions based on the CelebA image data set.
Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris
Such techniques are designed to restore generic images and therefore do not exploit the specific structure found in biometric images (e. g. iris or faces), which causes the solution to be sub-optimal.
Improving Super-Resolution Performance using Meta-Attention Layers
While many such networks can upscale low-resolution (LR) images using just the raw pixel-level information, the ill-posed nature of SR can make it difficult to accurately super-resolve an image which has undergone multiple different degradations.
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields
Super-resolving this principal basis using an SISR method allows us to super-resolve all the information that is coherent across the entire light field.
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
To gain a better understanding of the variation we find in face description and the possible issues that this may raise, we also conducted an annotation study on a subset of the corpus.
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks
Light field imaging has recently known a regain of interest due to the availability of practical light field capturing systems that offer a wide range of applications in the field of computer vision.
