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Found 6 papers, 0 papers with code
Learning Event Guided High Dynamic Range Video Reconstruction
Limited by the trade-off between frame rate and exposure time when capturing moving scenes with conventional cameras, frame based HDR video reconstruction suffers from scene-dependent exposure ratio balancing and ghosting artifacts.
All-in-Focus Imaging From Event Focal Stack
Given an RGB image focused at an arbitrary distance, we explore the high temporal resolution of event streams, from which we automatically select refocusing timestamps and reconstruct corresponding refocused images with events to form a focal stack.
Coherent Event Guided Low-Light Video Enhancement
With frame-based cameras, capturing fast-moving scenes without suffering from blur often comes at the cost of low SNR and low contrast.
BiSTNet: Semantic Image Prior Guided Bidirectional Temporal Feature Fusion for Deep Exemplar-based Video Colorization
To overcome this problem, we further develop a mixed expert block to extract semantic information for modeling the object boundaries of frames so that the semantic image prior can better guide the colorization process for better performance.
EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-Resolution
To reconstruct high-resolution intensity images from event data, we propose EvIntSR-Net that converts event data to multiple latent intensity frames to achieve super-resolution on intensity images in this paper.
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding.
