3 citations · 16 across the 18 of their papers we have counts for
18 papers
Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation
Xiaole Zhao, Linze Li, Chengxing Xie +5
Transformer-based deep models for single image super-resolution (SISR) have greatly improved the performance of lightweight SISR tasks in recent years. However, they often suffer f…
GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval
Han Zhou, Wei Dong, Xiaohong Liu +4
Most existing Low-light Image Enhancement (LLIE) methods either directly map Low-Light (LL) to Normal-Light (NL) images or use semantic or illumination maps as guides. However, the…
LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models
Hai Jiang, Ao Luo, Xiaohong Liu +2
In this paper, we propose a diffusion-based unsupervised framework that incorporates physically explainable Retinex theory with diffusion models for low-light image enhancement, na…
Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder
Jia Liu, Changlin Li, Qirui Sun +5
Fine-tuning advanced diffusion models for high-quality image stylization usually requires large training datasets and substantial computational resources, hindering their practical…
RecDiffusion: Rectangling for Image Stitching with Diffusion Models
Tianhao Zhou, Haipeng Li, Ziyi Wang +5
Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing. To solve non-rectangular boundaries, current solutions i…
GAFlow: Incorporating Gaussian Attention into Optical Flow
Ao Luo, Fan Yang, Xin Li +4
Optical flow, or the estimation of motion fields from image sequences, is one of the fundamental problems in computer vision. Unlike most pixel-wise tasks that aim at achieving con…