arXiv:1710.02213
Video Denoising and Enhancement via Dynamic Video Layering
원문 초록
Video denoising refers to the problem of removing "noise" from a video sequence. Here the term "noise" is used in a broad sense to refer to any corruption or outlier or interference that is not the quantity of interest. In this work, we develop a novel approach to video denoising that is based on the idea that many noisy or corrupted videos can be split into three parts - the "low-rank layer", the "sparse layer", and a small residual (which is small and bounded). We show, using extensive experiments, that our denoising approach outperforms the state-of-the-art denoising algorithms.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MotionPro: A Precise Motion Controller for Image-to-Video Generation
CIM-NET: A Video Denoising Deep Neural Network Model Optimized for Computing-in-Memory Architectures
Refining Few-Step Text-to-Multiview Diffusion via Reinforcement Learning
On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion Models
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.