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 …

Standard digital cameras typically take images with under/overexposed regions because of
their sensors' limited dynamic range. The most common way to capture high dynamic range …

High dynamic range (HDR) image generation and display technologies are becoming
increasingly popular in various applications. A standard and commonly used approach to …

Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …

Obtaining a high quality high dynamic range (HDR) image in the presence of camera and
object movement has been a long‐standing challenge. Many methods, known as HDR …

We propose a simple yet effective structural patch decomposition approach for multi-
exposure image fusion (MEF) that is robust to ghosting effect. We decompose an image …

This paper proposes the first non-flow-based deep framework for high dynamic range (HDR)
imaging of dynamic scenes with large-scale foreground motions. In state-of-the-art deep …

One of the most challenging problems in reconstructing a high dynamic range (HDR) image
from multiple low dynamic range (LDR) inputs is the ghosting artifacts caused by the object …

In this paper, we present an attention-guided deformable convolutional network for hand-
held multi-frame high dynamic range (HDR) imaging, namely ADNet. This problem …

Abstract High-dynamic-range (HDR) imaging is crucial for many applications. Yet, acquiring
HDR images with a single shot remains a challenging problem. Whereas modern deep …