Multimodal image super-resolution (SR) is the reconstruction of a high resolution image given a low-resolution observation with the aid of another image modality.
The deep unfolding architecture is used as a core component of a multimodal framework for guided image super-resolution.
Deep learning methods have been successfully applied to various computer vision tasks.
In linear inverse problems, the goal is to recover a target signal from undersampled, incomplete or noisy linear measurements.
Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e. g., meteorological and traffic information.
Specifically, we propose an autoencoder-based matrix completion model that performs prediction of the unknown matrix values as a main task, and manifold learning as an auxiliary task.
Matrix completion is one of the key problems in signal processing and machine learning, with applications ranging from image pro- cessing and data gathering to classification and recommender sys- tems.
Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far.
In the context of Twitter user geolocation, we realize MENET with textual, network, and metadata features.
Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism implemented by an appropriate projection matrix.