3 citations · 6 across the 6 of their papers we have counts for
6 papers · 1 filter
DMAligner: Enhancing Image Alignment via Diffusion Model Based View Synthesis
Xinglong Luo, Ao Luo, Zhengning Wang +5
Image alignment is a fundamental task in computer vision with broad applications. Existing methods predominantly employ optical flow-based image warping. However, this technique is…
Learning Efficient Meshflow and Optical Flow from Event Cameras
Xinglong Luo, Ao Luo, Kunming Luo +4
In this paper, we explore the problem of event-based meshflow estimation, a novel task that involves predicting a spatially smooth sparse motion field from event cameras. To start,…
FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection
Jianwei Zhao, Xin Li, Fan Yang +4
Detecting objects seamlessly blended into their surroundings represents a complex task for both human cognitive capabilities and advanced artificial intelligence algorithms. Curren…
LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models
Hai Jiang, Ao Luo, Xiaohong Liu +2
In this paper, we propose a diffusion-based unsupervised framework that incorporates physically explainable Retinex theory with diffusion models for low-light image enhancement, na…
RecDiffusion: Rectangling for Image Stitching with Diffusion Models
Tianhao Zhou, Haipeng Li, Ziyi Wang +5
Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing. To solve non-rectangular boundaries, current solutions i…
GAFlow: Incorporating Gaussian Attention into Optical Flow
Ao Luo, Fan Yang, Xin Li +4
Optical flow, or the estimation of motion fields from image sequences, is one of the fundamental problems in computer vision. Unlike most pixel-wise tasks that aim at achieving con…