13 citations · 35 across the 5 of their papers we have counts for
5 papers
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Andrey Ignatov, Grigory Malivenko, Radu Timofte +36
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus…
MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network
Hongtao Wang, Jiangshe Zhang, Xiaoli Wei +4
Picking the first arrival times of prestack gathers is called First Arrival Time (FAT) picking, which is an indispensable step in seismic data processing, and is mainly solved manu…
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Grigory Malivenko, David Plowman +35
Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually…
MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion
Yicheng Wang, Shuang Xu, Junmin Liu +3
Multi-Focus Image Fusion (MFIF) is a promising image enhancement technique to obtain all-in-focus images meeting visual needs and it is a precondition of other computer vision task…
Deep Convolutional Sparse Coding Networks for Image Fusion
Shuang Xu, Zixiang Zhao, Yicheng Wang +3
Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged…