1 code implementation • 8 Apr 2022 • Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Andreea-Iuliana Miron, Olivian Savencu, Nicolae-Catalin Ristea, Nicolae Verga, Fahad Shahbaz Khan
Our attention module uses the convolution operation to perform joint spatial-channel attention on multiple concatenated input tensors, where the kernel (receptive field) size controls the reduction rate of the spatial attention, and the number of convolutional filters controls the reduction rate of the channel attention, respectively.
Ranked #1 on Image Super-Resolution on IXI
1 code implementation • 12 Oct 2021 • Nicolae-Catalin Ristea, Andreea-Iuliana Miron, Olivian Savencu, Mariana-Iuliana Georgescu, Nicolae Verga, Fahad Shahbaz Khan, Radu Tudor Ionescu
Our neural model can be trained on unpaired images, due to the integration of a multi-level cycle-consistency loss.
1 code implementation • 1 Aug 2020 • Mihail Burduja, Radu Tudor Ionescu, Nicolae Verga
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge.
1 code implementation • 5 Jan 2020 • Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Nicolae Verga
We evaluate our method in the context of 2D and 3D super-resolution of CT and MRI scans from two databases, comparing it to relevant related works from the literature and baselines based on various interpolation schemes, using 2x and 4x scaling factors.
Ranked #6 on Image Super-Resolution on IXI