17 citations · 80 across the 13 of their papers we have counts for
13 papers
HDRT: A Large-Scale Dataset for Infrared-Guided HDR Imaging
Jingchao Peng, Thomas Bashford-Rogers, Francesco Banterle +2
Capturing images with enough details to solve imaging tasks is a long-standing challenge in imaging, particularly due to the limitations of standard dynamic range (SDR) images whic…
Semantic Aware Diffusion Inverse Tone Mapping
Abhishek Goswami, Aru Ranjan Singh, Francesco Banterle +2
The range of real-world scene luminance is larger than the capture capability of many digital camera sensors which leads to details being lost in captured images, most typically in…
Virtual Category Learning: A Semi-Supervised Learning Method for Dense Prediction with Extremely Limited Labels
Changrui Chen, Jungong Han, Kurt Debattista
Due to the costliness of labelled data in real-world applications, semi-supervised learning, underpinned by pseudo labelling, is an appealing solution. However, handling confusing…
SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems
Amir Samadi, Amir Shirian, Konstantinos Koufos +2
A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement. In other words, a CF explainer computes the minimum modifica…
Semi-supervised Object Detection via Virtual Category Learning
Changrui Chen, Kurt Debattista, Jungong Han
Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing. However, handling confusing sam…
Unsupervised HDR Imaging: What Can Be Learned from a Single 8-bit Video?
Francesco Banterle, Demetris Marnerides, Kurt Debattista +1
Recently, Deep Learning-based methods for inverse tone-mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular. These methods…
Deep HDR Hallucination for Inverse Tone Mapping
Demetris Marnerides, Thomas Bashford-Rogers, Kurt Debattista
Inverse Tone Mapping (ITM) methods attempt to reconstruct High Dynamic Range (HDR) information from Low Dynamic Range (LDR) image content. The dynamic range of well-exposed areas m…
Deep Controllable Backlight Dimming
Lvyin Duan, Demetris Marnerides, Alan Chalmers +2
Dual-panel displays require local dimming algorithms in order to reproduce content with high fidelity and high dynamic range. In this work, a novel deep learning based local dimmin…
Spectrally Consistent UNet for High Fidelity Image Transformations
Demetris Marnerides, Thomas Bashford-Rogers, Kurt Debattista
Convolutional Neural Networks (CNNs) are the current de-facto models used for many imaging tasks due to their high learning capacity as well as their architectural qualities. The u…
Audio-Visual-Olfactory Resource Allocation for Tri-modal Virtual Environments
Efstratios Doukakis, Kurt Debattista, Thomas Bashford-Rogers +4
Virtual Environments (VEs) provide the opportunity to simulate a wide range of applications, from training to entertainment, in a safe and controlled manner. For applications which…
Visuohaptic augmented feedback for enhancing motor skills acquisition
Ali Asadipour, Kurt Debattista, Alan Chalmers
Serious games are accepted as an effective approach to deliver augmented feedback in motor (re-) learning processes. The multi-modal nature of the conventional computer games (e.g.…
A Technology-aided Multi-modal Training Approach to Assist Abdominal Palpation Training and its Assessment in Medical Education
A. Asadipour, K. Debattista, V. Patel +1
Computer-assisted multimodal training is an effective way of learning complex motor skills in various applications. In particular disciplines (eg. healthcare) incompetency in perfo…
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content
Demetris Marnerides, Thomas Bashford-Rogers, Jonathan Hatchett +1
High dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represe…