Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image
processing and computer vision, which can integrate images with multiple exposure levels …

Multi-exposure image fusion (MEF) targets to integrate multiple shots with different
exposures and generates a single higher dynamic image than each. Existing deep learning …

Due to the poor lighting condition and limited dynamic range of digital imaging devices, the
recorded images are often under-/over-exposed and with low contrast. Most of previous …

We present a novel deep learning architecture for fusing static multi-exposure images.
Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input …

Exposure bracketing is crucial to high dynamic range imaging, but it is prone to halos for
static scenes and ghosting artifacts for dynamic scenes. The recently proposed structural …

Low-light image enhancement algorithms can improve the visual quality of low-light images
and support the extraction of valuable information for some computer vision techniques …

In this paper, we introduce a new approach for high-quality multi-exposure image fusion
(MEF). We show that the fusion weights of an exposure can be encoded into a 1D lookup …

Due to the complexity of real environment and potential defects of current simulation
datasets, either prior-based or deep learning-based single image dehazing methods may …

Nowadays, people are getting used to taking photos to record their daily life, however, the
photos are actually not consistent with the real natural scenes. The two main differences are …

Model-based single image dehazing was widely studied due to its extensive applications.
Ambiguity between object radiance and haze and noise amplification in sky regions are two …