5 accepted papers
Reconstruction of hyperspectral images (HSIs) from their RGB measurements is an ill-posed inverse problem. The key to successful reconstruction relies on establishing an effective HSI prior, for which deep learning techniques have achieved impressive performance. However, the scarcity of large-scale…
"Recent advances in deep learning for event-driven applications with dynamic vision sensors (DVS) primarily rely on training over simulated data. However, most simulators ignore various physics-based characteristics of real DVS, such as the fidelity of event timestamps and comprehensive noise effect…
Blind deblurring has received considerable attention in recent years. However, state-of-the-art methods often fail to process saturated blurry images. The main reason is that saturated pixels are not conforming to the commonly used linear blur model. Pioneer arts suggest excluding saturated pixels d…
Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels. In this paper, we propose a non-blind deblurring network (NBDN) to restore night blurry images. To mitig…
Event-based sensors, which have a response if the change of pixel intensity exceeds a triggering threshold, can capture high-speed motion with microsecond accuracy. Assisted by an event camera, we can generate high frame-rate sharp videos from low frame-rate blurry ones captured by an intensity came…