28 citations · 45 across the 6 of their papers we have counts for
6 papers
Unleashing the Potential of the Semantic Latent Space in Diffusion Models for Image Dehazing
Zizheng Yang, Hu Yu, Bing Li +3
Diffusion models have recently been investigated as powerful generative solvers for image dehazing, owing to their remarkable capability to model the data distribution. However, th…
Uncovering the Text Embedding in Text-to-Image Diffusion Models
Hu Yu, Hao Luo, Fan Wang +1
The correspondence between input text and the generated image exhibits opacity, wherein minor textual modifications can induce substantial deviations in the generated image. While,…
Unmasking Bias in Diffusion Model Training
Hu Yu, Li Shen, Jie Huang +2
Denoising diffusion models have emerged as a dominant approach for image generation, however they still suffer from slow convergence in training and color shift issues in sampling.…
High-quality Image Dehazing with Diffusion Model
Hu Yu, Jie Huang, Kaiwen Zheng +1
Image dehazing is quite challenging in dense-haze scenarios, where quite less original information remains in the hazy image. Though previous methods have made marvelous progress,…
Deep Fourier Up-Sampling
Man Zhou, Hu Yu, Jie Huang +5
Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling. However, spatial up-sampling operators (\emph{e.g.}, interpolation, transpos…
Source-Free Domain Adaptation for Real-world Image Dehazing
Hu Yu, Jie Huang, Yajing Liu +3
Deep learning-based source dehazing methods trained on synthetic datasets have achieved remarkable performance but suffer from dramatic performance degradation on real hazy images…