10 papers · 1 filter
VTinker: Guided Flow Upsampling and Texture Mapping for High-Resolution Video Frame Interpolation
Chenyang Wu, Jiayi Fu, Chun-Le Guo +2
Due to large pixel movement and high computational cost, estimating the motion of high-resolution frames is challenging. Thus, most flow-based Video Frame Interpolation (VFI) metho…
FlowLUT: Efficient Image Enhancement via Differentiable LUTs and Iterative Flow Matching
Liubing Hu, Chen Wu, Anrui Wang +3
Deep learning-based image enhancement methods face a fundamental trade-off between computational efficiency and representational capacity. For example, although a conventional thre…
UHD Image Dehazing via anDehazeFormer with Atmospheric-aware KV Cache
Pu Wang, Pengwen Dai, Chen Wu +5
In this paper, we propose an efficient visual transformer framework for ultra-high-definition (UHD) image dehazing that addresses the key challenges of slow training speed and high…
Distribution-aware Dataset Distillation for Efficient Image Restoration
Zhuoran Zheng, Xin Su, Chen Wu +1
With the exponential increase in image data, training an image restoration model is laborious. Dataset distillation is a potential solution to this problem, yet current distillatio…
AdaQual-Diff: Diffusion-Based Image Restoration via Adaptive Quality Prompting
Xin Su, Chen Wu, Yu Zhang +2
Restoring images afflicted by complex real-world degradations remains challenging, as conventional methods often fail to adapt to the unique mixture and severity of artifacts prese…
UniFlowRestore: A General Video Restoration Framework via Flow Matching and Prompt Guidance
Shuning Sun, Yu Zhang, Chen Wu +4
Video imaging is often affected by complex degradations such as blur, noise, and compression artifacts. Traditional restoration methods follow a "single-task single-model" paradigm…