Academic Articles & Research Papers

Impact of super resolution deep learning reconstruction with a 1024 matrix on CAD-RADS classification of intermediate coronary stenosis: A bicentric retrospective study.

Computer vision applied to super resolution

Super-resolution (SR) restoration aims to solve the following problem: given a set of observed images, estimate an image at a higher resolution than is present in any of the individual images. Where the application of this technique differs...

Downscaling atmospheric chemistry simulations with physically consistent deep learning

Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrat...

Semantic-Aware Guided Low-Light Image Super-Resolution

The single image super-resolution based on deep learning has achieved extraordinary performance. However, due to inevitable environmental or technological limitations, some images not only have low resolution but also low brightness. The ex...