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Found 9 papers, 1 papers with code
Physically Inspired Dense Fusion Networks for Relighting
While our proposed method applies to both one-to-one and any-to-any relighting problems, for each case we introduce problem-specific components that enrich the model performance: 1) For one-to-one relighting we incorporate normal vectors of the surfaces in the scene to adjust gloss and shadows accordingly in the image.
Ranked #1 on
Image Relighting
on VIDIT’20 validation set
NTIRE 2020 Challenge on NonHomogeneous Dehazing
We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor.
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors
Sharpness is emphasized by the variance of the Laplacian which we show can be implemented by a fixed feedback layer at the output of the network.
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks
As the first contribution, we show that DCT can be integrated into the network structure as a Convolutional DCT (CDCT) layer.
Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection
Using a set of canonical cell nuclei shapes, prepared with the help of a domain expert, we develop a new approach that we call Shape Priors with Convolutional Neural Networks (SP-CNN).
Deep MR Image Super-Resolution Using Structural Priors
High resolution magnetic resonance (MR) images are desired for accurate diagnostics.
Deep Image Super Resolution via Natural Image Priors
Single image super-resolution (SR) via deep learning has recently gained significant attention in the literature.
Orthogonally Regularized Deep Networks For Image Super-resolution
Deep learning methods, in particular trained Convolutional Neural Networks (CNNs) have recently been shown to produce compelling state-of-the-art results for single image Super-Resolution (SR).
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery
The classification problem has been firstly, and partially, addressed by sparse representation-based classification (SRC) method which can extract noise from signals and exploit the cross-channel information.
