Results 11 to 20 of about 7,150,154 (369)
SwinIR: Image Restoration Using Swin Transformer [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images).
Jingyun Liang +5 more
semanticscholar +1 more source
Restormer: Efficient Transformer for High-Resolution Image Restoration [PDF]
Computer Vision and Pattern Recognition, 2021 Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Syed Waqas Zamir +5 more
semanticscholar +1 more source
Multi-Stage Progressive Image Restoration [PDF]
Computer Vision and Pattern Recognition, 2021 Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synergistic design that can optimally balance these competing goals.
Syed Waqas Zamir +6 more
semanticscholar +1 more source
Simple Baselines for Image Restoration [PDF]
European Conference on Computer Vision, 2022 Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods.
Liangyu Chen +3 more
semanticscholar +1 more source
Uformer: A General U-Shaped Transformer for Image Restoration [PDF]
Computer Vision and Pattern Recognition, 2021 In this paper, we present Uformer, an effective and efficient Transformer-based architecture for image restoration, in which we build a hierarchical encoder-decoder network using the Transformer block. In Uformer, there are two core designs.
Zhendong Wang +3 more
semanticscholar +1 more source
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior [PDF]
arXiv.org, 2023 We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework. DiffBIR decouples blind image restoration problem into two stages: 1) degradation removal: removing image-independent ...
X. Lin +8 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
Retracted: Digital Image Restoration Based on Multicontour Batch Scanning. [PDF]
Scanning, 2023 Scanning.
europepmc +3 more sources
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
Learning Enriched Features for Fast Image Restoration and Enhancement [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Given a degraded input image, image restoration aims to recover the missing high-quality image content. Numerous applications demand effective image restoration, e.g., computational photography, surveillance, autonomous vehicles, and remote sensing ...
Syed Waqas Zamir +6 more
semanticscholar +1 more source