Search Results for author: Gregory Slabaugh

Found 28 papers, 10 papers with code

More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning

1 code implementation ECCV 2020 Yu Liu, Sarah Parisot, Gregory Slabaugh, Xu Jia, Ales Leonardis, Tinne Tuytelaars

Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints.

Incremental Learning

Wavelet-Based Dual-Branch Network for Image Demoiréing

no code implementations ECCV 2020 Lin Liu, Jianzhuang Liu, Shanxin Yuan, Gregory Slabaugh, Aleš Leonardis, Wengang Zhou, Qi Tian

When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.

Image Restoration Rain Removal

Vector Quantized Semantic Communication System

no code implementations23 Sep 2022 Qifan Fu, Huiqiang Xie, Zhijin Qin, Gregory Slabaugh, Xiaoming Tao

Although analog semantic communication systems have received considerable attention in the literature, there is less work on digital semantic communication systems.

Quantization SSIM

FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging

no code implementations7 Jan 2022 Sibi Catley-Chandar, Thomas Tanay, Lucas Vandroux, Aleš Leonardis, Gregory Slabaugh, Eduardo Pérez-Pellitero

We introduce a strategy that learns to jointly align and assess the alignment and exposure reliability using an HDR-aware, uncertainty-driven attention map that robustly merges the frames into a single high quality HDR image.

Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs

no code implementations NeurIPS 2020 Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory Slabaugh, Qi Tian

In this paper, we propose a self-adaptive learning method for demoiréing a high-frequency image, with the help of an additional defocused moiré-free blur image.

Learning to Sample the Most Useful Training Patches from Images

no code implementations24 Nov 2020 Shuyang Sun, Liang Chen, Gregory Slabaugh, Philip Torr

Some image restoration tasks like demosaicing require difficult training samples to learn effective models.

Demosaicking

Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs

1 code implementation3 Nov 2020 Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory Slabaugh, Qi Tian

In this paper, we propose a self-adaptive learning method for demoireing a high-frequency image, with the help of an additional defocused moire-free blur image.

Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping

1 code implementation ECCV 2020 Danai Triantafyllidou, Sean Moran, Steven McDonagh, Sarah Parisot, Gregory Slabaugh

Advances in low-light video RAW-to-RGB translation are opening up the possibility of fast low-light imaging on commodity devices (e. g. smartphone cameras) without the need for a tripod.

Image and Video Processing

Wavelet-Based Dual-Branch Network for Image Demoireing

no code implementations14 Jul 2020 Lin Liu, Jianzhuang Liu, Shanxin Yuan, Gregory Slabaugh, Ales Leonardis, Wengang Zhou, Qi Tian

When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.

Image Restoration Rain Removal

DeepLPF: Deep Local Parametric Filters for Image Enhancement

2 code implementations CVPR 2020 Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, Gregory Slabaugh

We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image.

Image Enhancement

Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem

1 code implementation CVPR 2020 Matthias De Lange, Xu Jia, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars

This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.

Continual Learning Domain Adaptation +2

A Multi-Hypothesis Approach to Color Constancy

1 code implementation CVPR 2020 Daniel Hernandez-Juarez, Sarah Parisot, Benjamin Busam, Ales Leonardis, Gregory Slabaugh, Steven McDonagh

Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate of set of corrected images.

Color Constancy

Reconstructing the Noise Manifold for Image Denoising

no code implementations11 Feb 2020 Ioannis Marras, Grigorios G. Chrysos, Ioannis Alexiou, Gregory Slabaugh, Stefanos Zafeiriou

Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising.

Conditional Image Generation Image Denoising +1

CURL: Neural Curve Layers for Global Image Enhancement

4 code implementations29 Nov 2019 Sean Moran, Steven McDonagh, Gregory Slabaugh

We present a novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool.

Demosaicking Denoising +2

Pixel Adaptive Filtering Units

no code implementations24 Nov 2019 Filippos Kokkinos, Ioannis Marras, Matteo Maggioni, Gregory Slabaugh, Stefanos Zafeiriou

Next, we employ PAFU in deep neural networks as a replacement of standard convolutional layers to enhance the original architectures with spatially varying computations to achieve considerable performance improvements.

Translation

AIM 2019 Challenge on Image Demoireing: Dataset and Study

no code implementations6 Nov 2019 Shanxin Yuan, Radu Timofte, Gregory Slabaugh, Ales Leonardis

In addition to describing the dataset and its creation, this paper also reviews the challenge tracks, competition, and results, the latter summarizing the current state-of-the-art on this dataset.

Image Manipulation

SteReFo: Efficient Image Refocusing with Stereo Vision

no code implementations29 Sep 2019 Benjamin Busam, Matthieu Hog, Steven McDonagh, Gregory Slabaugh

Whether to attract viewer attention to a particular object, give the impression of depth or simply reproduce human-like scene perception, shallow depth of field images are used extensively by professional and amateur photographers alike.

Depth Estimation

A continual learning survey: Defying forgetting in classification tasks

1 code implementation18 Sep 2019 Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.

Classification Continual Learning +2

NODE: Extreme Low Light Raw Image Denoising using a Noise Decomposition Network

no code implementations11 Sep 2019 Hao Guan, Liu Liu, Sean Moran, Fenglong Song, Gregory Slabaugh

In this paper, we propose a multi-task deep neural network called Noise Decomposition (NODE) that explicitly and separately estimates defective pixel noise, in conjunction with Gaussian and Poisson noise, to denoise an extreme low light image.

Image Denoising

Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem

no code implementations28 Nov 2018 Steven McDonagh, Sarah Parisot, Fengwei Zhou, Xing Zhang, Ales Leonardis, Zhenguo Li, Gregory Slabaugh

In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets.

Few-Shot Camera-Adaptive Color Constancy Meta-Learning

Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models

no code implementations23 Aug 2018 Nathan J Olliverre, Guang Yang, Gregory Slabaugh, Constantino Carlos Reyes-Aldasoro, Eduardo Alonso

Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality.

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