Results 1 to 10 of about 272,612 (359)
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection [PDF]
International Conference on Learning Representations, 2022 We present DINO (\textbf{D}ETR with \textbf{I}mproved de\textbf{N}oising anch\textbf{O}r boxes), a state-of-the-art end-to-end object detector. % in this paper.
Hao Zhang +7 more
semanticscholar +1 more source
RePaint: Inpainting using Denoising Diffusion Probabilistic Models [PDF]
Computer Vision and Pattern Recognition, 2022 Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask ...
Andreas Lugmayr +5 more
semanticscholar +1 more source
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising [PDF]
Computer Vision and Pattern Recognition, 2022 We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods.
Feng Li +5 more
semanticscholar +1 more source
Denoising Diffusion Models for Plug-and-Play Image Restoration [PDF]
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023 Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser as the implicit image prior.
Yuanzhi Zhu +6 more
semanticscholar +1 more source
Hangkong gongcheng jinzhan, 2023 The effect of point cloud denoising is very important to the subsequent surface fitting and modeling design in 3D scanning process. How to extract feature points quickly and accurately has become a research hotspot.However,the key point of point cloud ...
LI Binpeng, MAO Jian, YANG Jie, CAI Hang
doaj +1 more source
DiGress: Discrete Denoising diffusion for graph generation [PDF]
International Conference on Learning Representations, 2022 This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process that progressively edits graphs with noise, through the process of adding ...
Clément Vignac +5 more
semanticscholar +1 more source
Overview of Image Denoising Methods
Jisuanji kexue yu tansuo, 2021 In real scenes, due to the imperfections of equipment and systems or the existence of low-light environments, the collected images are noisy. The images will also be affected by additional noise during the compression and transmission process, which will
LIU Liping, QIAO Lele, JIANG Liucheng
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Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model [PDF]
International Conference on Learning Representations, 2022 Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators. In this work, we propose the Denoising Diffusion Null-Space Model (DDNM), a novel zero-shot framework for arbitrary linear IR ...
Yinhuai Wang, Jiwen Yu, Jian Zhang
semanticscholar +1 more source
Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Image restoration under adverse weather conditions has been of significant interest for various computer vision applications. Recent successful methods rely on the current progress in deep neural network architectural designs (e.g., with vision ...
Ozan Özdenizci, R. Legenstein
semanticscholar +1 more source
Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
Applied Sciences, 2023 In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition.
Roopdeep Kaur +2 more
doaj +1 more source