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Found 12 papers, 2 papers with code
LORD: Leveraging Open-Set Recognition with Unknown Data
To mitigate dependency on extensive and costly background datasets, we explore mixup as an off-the-shelf data generation technique.
Exploring the Open World Using Incremental Extreme Value Machines
One particular machine learning problem in dynamic environments is open world recognition.
Joint Super-Resolution and Rectification for Solar Cell Inspection
Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants.
Merging-ISP: Multi-Exposure High Dynamic Range Image Signal Processing
As such, it is end-to-end trainable, circumvents the use of hand-crafted and potentially complex algorithms, and mitigates error propagation.
Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging
The optical resolution of a digital camera is one of its most crucial parameters with broad relevance for consumer electronics, surveillance systems, remote sensing, or medical imaging.
Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data
One interesting result is that top-performing methods on simulated data may be surpassed by others on real data.
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests
We customize features suitable for volumetric MRI to train the random forest and propose a median tree ensemble for robust regression.
Temporal and volumetric denoising via quantile sparse image prior
For the purpose of denoising, we propose a variational framework based on the QuaSI prior and a Huber data fidelity model that can handle 3-D and 3-D+t data.
Benchmarking Super-Resolution Algorithms on Real Data
Therefore, current studies are either evaluated 1) on simulated data or 2) on real data without a pixel-wise ground truth.
QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data
Optical coherence tomography (OCT) enables high-resolution and non-invasive 3D imaging of the human retina but is inherently impaired by speckle noise.
Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution
Motion estimation across low-resolution frames and the reconstruction of high-resolution images are two coupled subproblems of multi-frame super-resolution.
Super-Resolved Retinal Image Mosaicing
The acquisition of high-resolution retinal fundus images with a large field of view (FOV) is challenging due to technological, physiological and economic reasons.
