CCSR: Improving the Stability of Diffusion Models for Content Consistent Super-Resolution 港理工张磊组的工作,同时是SeeSR论文部分成员的工作。旨在提升SR中Diffusion Model的稳定性,核心思想还是DM在底层视觉的关键问题:Fidelity-Realness Trade-off。 arxiv:https://arxiv.org/pdf/2401.00877.pdf Motivation Diffusi...
4. Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution(南洋理工 Chen Change Loy团队)视频放大: 用于真实世界视频超分辨率的时间一致性扩散模型 Paper: arXiv, CVPR 2024 Open Access Repository Code: github.com/sczhou/Upsca Project Page: Upscale-A-Video for Video...
而神经网络可以对某个函数进行逼近得到近似解,所以我们可以训练一个神经网络对图像进行近似表示。 2. 去噪扩散模型(DDPM - Denoising Diffusion Probabilistic Model 扩散概率模型)--后面出个时间写下这个论文的笔记 扩散是指物质粒子从高浓度区域向低浓度区域移动的过程,扩散模型想做的就是通过向图片中加入高斯噪声模拟...
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real World Paper: Code: Keywords: Real World; Arbitrary-Scale 特殊场景 总结 从本届接收的论文来看,Diffusion model和Text引入语义文本信息是大的热点,单纯的超分基本已经绝迹,一般必须带上特殊场景或背景。
To alleviate the above problems, we propose the generative Diffusion Model with Detail Complement (DMDC) for RS super-resolution. Firstly, unlike traditional optimization models with insufficient image understanding, we introduce the diffusion model as a generation model into RSSR tasks and r...
Blog:论文笔记:Pixel-Aware Stable Diffusion for Realistic Image Super-Resolution and Personalized Stylization 12 Spatially-Variant Degradation Model for Dataset-free Super-resolution Paper:https://arxiv.org/abs/2407.08252 Code:https://github.com/shaojieguoECNU/SVDSR ...
1. MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces(哈工大 刘明、左旺孟团队) Paper:ICCV 2023 Open Access Repository Code:https://github.com/yinzhicun/MetaF2N 2. DARSR | Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Su...
This work introduces a model-based super-resolution reconstruction (SRR) technique for achieving high-resolution diffusion-weighted MRI. Diffusion-weighted imaging (DWI) is a key technique for investigating white matter non-invasively. However, due to hardware and imaging time constraints, the technique...
Therefore, we implement generative deep learning models to link low-cost, low-resolution images of the build plate to detailed high-resolution optical images of the build plate, enabling cost-efficient process monitoring. To do so, a conditional latent probabilistic diffusion model is trained to ...
A super-resolution (SR) technique is explored to reconstruct high-resolution images ( 4imes 4imes ) from lower resolution images in an advection-diffusion model of atmospheric pollution plumes. SR performance is generally increased when the advection-diffusion equation constrains the NN in addition ...