activity
20142024
most citedFast, nonlocal and neural: a lightweight high quality solution to image denoising

29 citations · 44 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV2024

Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach

Yaofang Liu, Yumeng Ren, Xiaodong Cun +5

Diffusion models have revolutionized image generation, and their extension to video generation has shown promise. However, current video diffusion models~(VDMs) rely on a scalar ti…

cs.CV2024

A Formalization of Image Vectorization by Region Merging

Roy Y. He, Sung Ha Kang, Jean-Michel Morel

Image vectorization converts raster images into vector graphics composed of regions separated by curves. Typical vectorization methods first define the regions by grouping similar…

cs.CV2024

Adapting MIMO video restoration networks to low latency constraints

Valéry Dewil, Zhe Zheng, Arnaud Barral +7

MIMO (multiple input, multiple output) approaches are a recent trend in neural network architectures for video restoration problems, where each network evaluation produces multiple…

eess.IV20245 cited

How to Best Combine Demosaicing and Denoising?

Yu Guo, Qiyu Jin, Jean-Michel Morel +1

Image demosaicing and denoising play a critical role in the raw imaging pipeline. These processes have often been treated as independent, without considering their interactions. In…

cs.CV2024

Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery

Xavier Bou, Gabriele Facciolo, Rafael Grompone von Gioi +2

The goal of this paper is to perform object detection in satellite imagery with only a few examples, thus enabling users to specify any object class with minimal annotation. To thi…

eess.IV202429 cited

Fast, nonlocal and neural: a lightweight high quality solution to image denoising

Yu Guo, Axel Davy, Gabriele Facciolo +2

With the widespread application of convolutional neural networks (CNNs), the traditional model based denoising algorithms are now outperformed. However, CNNs face two problems. Fir…