Image fusion is a well-recognized and a conventional field of image processing. Image
fusion provides an efficient way of enhancing and combining pixel-level data resulting in …

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 …

Deep learning networks have recently demonstrated yielded impressive progress for multi-
exposure image fusion. However, how to restore realistic texture details while correcting …

Multi-exposure image fusion (MEF) is considered an effective quality enhancement
technique widely adopted in consumer electronics, but little work has been dedicated to the …

In this paper, we present an end-to-end architecture for multi-exposure image fusion based
on generative adversarial networks, termed as MEF-GAN. In our architecture, a generator …

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 …

Tone-map** operators (TMOs) that convert high dynamic range (HDR) to low dynamic
range (LDR) images provide practically useful tools for the visualization of HDR images on …

Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is
important to design an effective prior. For this purpose, we propose a novel image SR …

This paper follows the idea of divide and conquer to propose a multi-exposure fusion
network for the unsupervised generation of high dynamic range-like images. We develop a …

We propose a multi-exposure image fusion (MEF) algorithm by optimizing a novel objective
quality measure, namely the color MEF structural similarity (MEF-SSIM c) index. The design …