Search Results for author:
Found 4 papers, 2 papers with code
Decomposer: Semi-supervised Learning of Image Restoration and Image Decomposition
We present Decomposer, a semi-supervised reconstruction model that decomposes distorted image sequences into their fundamental building blocks - the original image and the applied augmentations, i. e., shadow, light, and occlusions.
DIAR: Deep Image Alignment and Reconstruction using Swin Transformers
When taking images of some occluded content, one is often faced with the problem that every individual image frame contains unwanted artifacts, but a collection of images contains all relevant information if properly aligned and aggregated.
SIDAR: Synthetic Image Dataset for Alignment & Restoration
Our data generation pipeline is customizable and can be applied to any existing dataset, serving as a data augmentation to further improve the feature learning of any existing method.
Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural Networks
A Siamese network is used for global feature extraction and metric learning, which gives an initial ranking of the landmark search.
