Abstract Mapping Low Dynamic Range (LDR) images with different exposures to High
Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to …

High dynamic range (HDR) imaging aims to retrieve information from multiple low-dynamic
range inputs to generate realistic output. The essence is to leverage the contextual …

High dynamic range (HDR) imaging is widely used in consumer photography, computer
game rendering, autonomous driving, and surveillance systems. Reconstructing ghosting …

Ghosting artifacts caused by moving objects or misalignments is a key challenge in high
dynamic range (HDR) imaging for dynamic scenes. Previous methods first register the input …

High Dynamic Range (HDR) images can be recovered from several Low Dynamic Range
(LDR) images by existing Deep Neural Networks (DNNs) techniques. Despite the …

Abstract Recovering ghost-free High Dynamic Range (HDR) images from multiple Low
Dynamic Range (LDR) images becomes challenging when the LDR images exhibit …

High dynamic range (HDR) deghosting algorithms aim to generate ghost-free HDR images
with realistic details. Restricted by the locality of the receptive field, existing CNN-based …

Ghosting artifacts caused by moving objects and misalignments are a key challenge in
constructing high dynamic range (HDR) images. Current methods first register the input low …

Abstract Generating High Dynamic Range (HDR) image in the presence of camera and
object motion is a tedious task. If uncorrected, these motions will manifest as ghosting …

While today's high dynamic range (HDR) image fusion algorithms are capable of blending
multiple exposures, the acquisition is often controlled so that the dynamic range within one …