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20182023
most citedSemi-Supervised Deep Learning for Abnormality Classification in Retinal Images

30 citations · 32 across the 3 of their papers we have counts for

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6 papers · 1 filter

cs.CV2023

Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation

Bruno Lecouat, Yann Dubois de Mont-Marin, Théo Bodrito +2

This paper introduces a novel approach to the fine alignment of images in a burst captured by a handheld camera. In contrast to traditional techniques that estimate two-dimensional…

cs.CV20211 cited

NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results

Goutam Bhat, Martin Danelljan, Radu Timofte +25

This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times hig…

cs.CV2021

Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts

Bruno Lecouat, Jean Ponce, Julien Mairal

This presentation addresses the problem of reconstructing a high-resolution image from multiple lower-resolution snapshots captured from slightly different viewpoints in space and…

cs.CV2020

A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding

Bruno Lecouat, Jean Ponce, Julien Mairal

We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and wh…

cs.CV2019

Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration

Bruno Lecouat, Jean Ponce, Julien Mairal

Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling. We propose a novel differentiable relaxation of joint sparsity that…

cs.CV201830 cited

Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images

Bruno Lecouat, Ken Chang, Chuan-Sheng Foo +7

Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require…