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 …

Avoiding the introduction of ghosts when synthesising LDR images as high dynamic range
(HDR) images is a challenging task. Convolutional neural networks (CNNs) are effective for …

Light field (LF) imaging is an effective way to enable immersive applications. However,
limited by the potential well capacity of the image sensor, the acquired LF images suffer from …

The reconstruction of high dynamic range (HDR) images from multi-exposure low dynamic
range (LDR) images in dynamic scenes presents significant challenges, especially in …

In high-dynamic-range (HDR) image reconstruction, the background offset among multiple
multi-exposure low-dynamic-range (LDR) images, wide-range movement of targets, and …

Due to sensor limitations, the light field (LF) images captured by the LF camera suffer from
low dynamic range and are prone to poor exposure. To solve this problem, combining multi …

Multi-exposure high dynamic range (HDR) imaging aims to generate an HDR image from
multiple low dynamic range (LDR) images. There are two main challenges in this task; …

High dynamic range (HDR) video rendering from low dynamic range (LDR) videos where
frames are of alternate exposures encounters significant challenges, due to the exposure …

In multi-exposure HDR imaging of dynamic scenes, two challenging problems need to be
addressed: one is the removal of ghost artifacts, and the other is the reconstruction of lost …

Generating a high-quality High Dynamic Range (HDR) image from several Low Dynamic
Range (LDR) images is a challenging task due to the ghost artifacts caused by large motion …