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Found 20 papers, 11 papers with code
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency.
Latent Feature-Guided Diffusion Models for Shadow Removal
Recovering textures under shadows has remained a challenging problem due to the difficulty of inferring shadow-free scenes from shadow images.
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation
Our conditional-task learning and distillation approach outperforms previous distillation methods, achieving a new state-of-the-art in producing high-quality images with very few steps (e. g., 1-4) across multiple tasks, including super-resolution, text-guided image editing, and depth-to-image generation.
T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified Visual Modalities
The model can be scaled to generate high-resolution data while unifying multiple modalities.
Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors
The improvements obtained by our method suggest that the priors can be incorporated as a general plugin for improving conditional diffusion models.
VIDM: Video Implicit Diffusion Models
Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images.
Ranked #11 on
Video Generation
on UCF-101
AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models
In recent years, various deep learning-based single image atmospheric turbulence mitigation methods, including CNN-based and GAN inversion-based, have been proposed in the literature which attempt to remove the distortion in the image.
Deep Semantic Statistics Matching (D2SM) Denoising Network
The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains.
A comparison of different atmospheric turbulence simulation methods for image restoration
In this paper, we systematically evaluate the effectiveness of various turbulence simulation methods on image restoration.
Thermal to Visible Image Synthesis under Atmospheric Turbulence
In this paper, we first investigate the problem with a turbulence simulation method on real-world thermal images.
LTT-GAN: Looking Through Turbulence by Inverting GANs
To mitigate the turbulence effect, in this paper, we propose the first turbulence mitigation method that makes use of visual priors encapsulated by a well-trained GAN.
SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom
Deep Neural Network (DNN) based super-resolution algorithms have greatly improved the quality of the generated images.
AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing
In this paper, we propose a new model, called Attention-Augmented Network (AttaNet), to capture both global context and multilevel semantics while keeping the efficiency high.
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background
Although the emergence of deep learning has greatly promoted the development of this field, crowd counting under cluttered background is still a serious challenge.
MDCN: Multi-scale Dense Cross Network for Image Super-Resolution
Among them, MDCB aims to detect multi-scale features and maximize the use of image features flow at different scales, HFDB focuses on adaptively recalibrate channel-wise feature responses to achieve feature distillation, and DRB attempts to reconstruct SR images with different upsampling factors in a single model.
Disentangle Perceptual Learning through Online Contrastive Learning
Perceptual learning approaches like perceptual loss are empirically powerful for such tasks but they usually rely on the pre-trained classification network to provide features, which are not necessarily optimal in terms of visual perception of image transformation.
HighEr-Resolution Network for Image Demosaicing and Enhancing
However, plenty of studies have shown that global information is crucial for image restoration tasks like image demosaicing and enhancing.
Progressive Feature Fusion Network for Realistic Image Dehazing
Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium.
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks
Recent works on single-image super-resolution are concentrated on improving performance through enhancing spatial encoding between convolutional layers.
Multi-scale Residual Network for Image Super-Resolution
Meanwhile, we let these features interact with each other to get the most efficacious image information, we call this structure Multi-scale Residual Block (MSRB).
