15 papers
Taming Sampling Perturbations with Variance Expansion Loss for Latent Diffusion Models
Qifan Li, Xingyu Zhou, Jinhua Zhang +2
Latent diffusion models have emerged as the dominant framework for high-fidelity and efficient image generation, owing to their ability to learn diffusion processes in compact late…
ATD: Improved Transformer with Adaptive Token Dictionary for Image Restoration
Leheng Zhang, Wei Long, Yawei Li +3
Recently, Transformers have gained significant popularity in image restoration tasks such as image super-resolution and denoising, owing to their superior performance. However, bal…
Guiding a Diffusion Transformer with the Internal Dynamics of Itself
Xingyu Zhou, Qifan Li, Xiaobin Hu +2
The diffusion model presents a powerful ability to capture the entire (conditional) data distribution. However, due to the lack of sufficient training and data to learn to cover lo…
Task-Aware Image Signal Processor for Advanced Visual Perception
Kai Chen, Jin Xiao, Leheng Zhang +2
In recent years, there has been a growing trend in computer vision towards exploiting RAW sensor data, which preserves richer information compared to conventional low-bit RGB image…
Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution
Qifan Li, Jiale Zou, Jinhua Zhang +3
Vector-quantized based models have recently demonstrated strong potential for visual prior modeling. However, existing VQ-based methods simply encode visual features with nearest c…
Learning Pixel-adaptive Multi-layer Perceptrons for Real-time Image Enhancement
Junyu Lou, Xiaorui Zhao, Kexuan Shi +1
Deep learning-based bilateral grid processing has emerged as a promising solution for image enhancement, inherently encoding spatial and intensity information while enabling effici…