Search Results for author: João F. C. Mota

Found 10 papers, 7 papers with code

The Rate-Distortion-Perception-Classification Tradeoff: Joint Source Coding and Modulation via Inverse-Domain GANs

no code implementations22 Dec 2023 Junli Fang, João F. C. Mota, Baoshan Lu, Weicheng Zhang, Xuemin Hong

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.

Generative Adversarial Network Image Compression +1

Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems

no code implementations6 Nov 2022 Rahul Mourya, João F. C. Mota

To overcome this, we propose a framework that transforms any DNN for inverse problems into a measurement-consistent one.

Astronomy Image Super-Resolution

Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization

1 code implementation13 Jun 2021 Marija Vella, BoWen Zhang, Wei Chen, João F. C. Mota

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.

Astronomy Hyperspectral Image Super-Resolution +1

Overcoming Measurement Inconsistency in Deep Learning for Linear Inverse Problems: Applications in Medical Imaging

1 code implementation29 Nov 2020 Marija Vella, João F. C. Mota

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

1 code implementation2 Apr 2020 Marija Vella, João F. C. Mota

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.

Image Super-Resolution SSIM

Single Image Super-Resolution via CNN Architectures and TV-TV Minimization

2 code implementations11 Jul 2019 Marija Vella, João F. C. Mota

Super-resolution (SR) is a technique that allows increasing the resolution of a given image.

Image Super-Resolution SSIM

Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled Dictionaries

1 code implementation25 Sep 2017 Pingfan Song, Xin Deng, João F. C. Mota, Nikos Deligiannis, Pier Luigi Dragotti, Miguel R. D. Rodrigues

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.

Dictionary Learning Image Super-Resolution

X-ray image separation via coupled dictionary learning

no code implementations20 May 2016 Nikos Deligiannis, João F. C. Mota, Bruno Cornelis, Miguel R. D. Rodrigues, Ingrid Daubechies

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.

Dictionary Learning

Distributed Optimization With Local Domains: Applications in MPC and Network Flows

1 code implementation8 May 2013 João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel

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

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