27 citations · 49 across the 8 of their papers we have counts for
10 papers
Learning-based Statistical Refinement for Denoising
Rihuan Ke
This work proposes a learning-based statistical refinement method for improving the denoising results of a given denoiser without knowing the precise noise distribution or accessin…
Vehicle detection from GSV imagery: Predicting travel behaviour for cycling and motorcycling using Computer Vision
Kyriaki, Kokka, Rahul Goel +7
Transportation influence health by shaping exposure to physical activity, air pollution and injury risk. Comparative data on cycling and motorcycling behaviours is scarce, particul…
TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
Zhongying Deng, Yanqi Chen, Lihao Liu +4
Traffic flow analysis is revolutionising traffic management. Qualifying traffic flow data, traffic control bureaus could provide drivers with real-time alerts, advising the fastest…
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning
Zhongying Deng, Rihuan Ke, Carola-Bibiane Schonlieb +1
Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data. To better exploit the unlabeled data the latest SSL methods use…
Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Data
Rihuan Ke
With recent deep learning based approaches showing promising results in removing noise from images, the best denoising performance has been reported in a supervised learning setup…
A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
Rihuan Ke, Angelica Aviles-Rivero, Saurabh Pandey +2
Semantic segmentation has been widely investigated in the community, in which the state of the art techniques are based on supervised models. Those models have reported unprecedent…
Unsupervised Image Restoration Using Partially Linear Denoisers
Rihuan Ke, Carola-Bibiane Schönlieb
Deep neural network based methods are the state of the art in various image restoration problems. Standard supervised learning frameworks require a set of noisy measurement and cle…
iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling
Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb
U-Nets have been established as a standard architecture for image-to-image learning problems such as segmentation and inverse problems in imaging. For large-scale data, as it for e…
Multi-task deep learning for image segmentation using recursive approximation tasks
Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis +3
Fully supervised deep neural networks for segmentation usually require a massive amount of pixel-level labels which are manually expensive to create. In this work, we develop a mul…
Learning to segment microscopy images with lazy labels
Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis +2
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instan…