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Found 10 papers, 7 papers with code
The Rate-Distortion-Perception-Classification Tradeoff: Joint Source Coding and Modulation via Inverse-Domain GANs
The joint source coding and modulation (JSCM) framework was enabled by recent developments in deep learning, which allows to automatically learn from data, and in an end-to-end fashion, the best compression codes and modulation schemes.
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems
To overcome this, we propose a framework that transforms any DNN for inverse problems into a measurement-consistent one.
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization
Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image.
Overcoming Measurement Inconsistency in Deep Learning for Linear Inverse Problems: Applications in Medical Imaging
We then propose a framework that post-processes the output of DNNs with an optimization algorithm that enforces measurement consistency.
Robust Single-Image Super-Resolution via CNNs and TV-TV Minimization
Single-image super-resolution is the process of increasing the resolution of an image, obtaining a high-resolution (HR) image from a low-resolution (LR) one.
Single Image Super-Resolution via CNN Architectures and TV-TV Minimization
Super-resolution (SR) is a technique that allows increasing the resolution of a given image.
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled Dictionaries
This paper proposes a new approach to construct a high-resolution (HR) version of a low-resolution (LR) image given another HR image modality as reference, based on joint sparse representations induced by coupled dictionaries.
X-ray image separation via coupled dictionary learning
In support of art investigation, we propose a new source sepa- ration method that unmixes a single X-ray scan acquired from double-sided paintings.
Compressed Sensing With Side Information: Geometrical Interpretation and Performance Bounds
We address the problem of Compressed Sensing (CS) with side information.
Distributed Optimization With Local Domains: Applications in MPC and Network Flows
Our contribution is a communication-efficient distributed algorithm that finds a vector $x^\star$ minimizing the sum of all the functions.
Optimization and Control Information Theory Information Theory
