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20202023
most citedLearning Regularized Multi-Scale Feature Flow for High Dynamic Range Imaging

3 citations · 5 across the 5 of their papers we have counts for

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6 papers · 1 filter

cs.CV2023

Improved Neural Radiance Fields Using Pseudo-depth and Fusion

Jingliang Li, Qiang Zhou, Chaohui Yu +4

Since the advent of Neural Radiance Fields, novel view synthesis has received tremendous attention. The existing approach for the generalization of radiance field reconstruction pr…

cs.CV2022★ 1 cited

Online Video Super-Resolution with Convolutional Kernel Bypass Graft

Jun Xiao, Xinyang Jiang, Ningxin Zheng +5

Deep learning-based models have achieved remarkable performance in video super-resolution (VSR) in recent years, but most of these models are less applicable to online video applic…

cs.CV2022

Deep Progressive Feature Aggregation Network for High Dynamic Range Imaging

Jun Xiao, Qian Ye, Tianshan Liu +2

High dynamic range (HDR) imaging is an important task in image processing that aims to generate well-exposed images in scenes with varying illumination. Although existing multi-exp…

cs.CV2022★ 3 cited

Learning Regularized Multi-Scale Feature Flow for High Dynamic Range Imaging

Qian Ye, Masanori Suganuma, Jun Xiao +1

Reconstructing ghosting-free high dynamic range (HDR) images of dynamic scenes from a set of multi-exposure images is a challenging task, especially with large object motion and oc…

cs.CV2021★ 1 cited

Progressive and Selective Fusion Network for High Dynamic Range Imaging

Qian Ye, Jun Xiao, Kin-man Lam +1

This paper considers the problem of generating an HDR image of a scene from its LDR images. Recent studies employ deep learning and solve the problem in an end-to-end fashion, lead…

cs.CV2020

Deep Multi-task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing

Rui Zhao, Tianshan Liu, Jun Xiao +2

Multi-task learning is an effective learning strategy for deep-learning-based facial expression recognition tasks. However, most existing methods take into limited consideration th…