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Found 7 papers, 3 papers with code
Uncovering the Text Embedding in Text-to-Image Diffusion Models
The correspondence between input text and the generated image exhibits opacity, wherein minor textual modifications can induce substantial deviations in the generated image.
Debias the Training of Diffusion Models
Diffusion models have demonstrated compelling generation quality by optimizing the variational lower bound through a simple denoising score matching loss.
High-quality Image Dehazing with Diffusion Model
The latter stage exploits the strong generation ability of DDPM to compensate for the haze-induced huge information loss, by working in conjunction with the physical modelling.
Ingredient-Oriented Multi-Degradation Learning for Image Restoration
Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation.
Visual Recognition-Driven Image Restoration for Multiple Degradation With Intrinsic Semantics Recovery
Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images.
Deep Fourier Up-Sampling
Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling.
Source-Free Domain Adaptation for Real-world Image Dehazing
Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the source dataset to reduce the gap between the source synthetic and target real domains.
