Zobrazeno 1 - 10
of 215
pro vyhledávání: '"I.4.4"'
Seismic data denoising is an important part of seismic data processing, which directly relate to the follow-up processing of seismic data. In terms of this issue, many authors proposed many methods based on rank reduction, sparse transformation, doma
Externí odkaz:
http://arxiv.org/abs/2408.13578
Autor:
Rao, Chen, Li, Guangyuan, Lan, Zehua, Sun, Jiakai, Luan, Junsheng, Xing, Wei, Zhao, Lei, Lin, Huaizhong, Dong, Jianfeng, Zhang, Dalong
Current video deblurring methods have limitations in recovering high-frequency information since the regression losses are conservative with high-frequency details. Since Diffusion Models (DMs) have strong capabilities in generating high-frequency de
Externí odkaz:
http://arxiv.org/abs/2408.13459
When dealing with seismic data, diffusion models often face challenges in adequately capturing local features and expressing spatial relationships. This limitation makes it difficult for diffusion models to remove noise from complex structures effect
Externí odkaz:
http://arxiv.org/abs/2408.06963
The main goal of this paper is to propose a new quaternion total variation regularization model for solving linear ill-posed quaternion inverse problems, which arise from three-dimensional signal filtering or color image processing. The quaternion to
Externí odkaz:
http://arxiv.org/abs/2408.03032
Blind image deconvolution (BID) is a classic yet challenging problem in the field of image processing. Recent advances in deep image prior (DIP) have motivated a series of DIP-based approaches, demonstrating remarkable success in BID. However, due to
Externí odkaz:
http://arxiv.org/abs/2407.14816
Object detection forms a key component in Unmanned Aerial Vehicles (UAVs) for completing high-level tasks that depend on the awareness of objects on the ground from an aerial perspective. In that scenario, adversarial patch attacks on an onboard obje
Externí odkaz:
http://arxiv.org/abs/2405.19179
Publikováno v:
Computer Vision and Image Understanding, Volume 233, 2023, 103718
Blind single image super-resolution (SISR) is a challenging task in image processing due to the ill-posed nature of the inverse problem. Complex degradations present in real life images make it difficult to solve this problem using na\"ive deep learn
Externí odkaz:
http://arxiv.org/abs/2404.16564
Autor:
Jiang, Wei, Wang, Wei
We propose a framework for learned image and video compression using the generative sparse visual representation (SVR) guided by fidelity-preserving controls. By embedding inputs into a discrete latent space spanned by learned visual codebooks, SVR-b
Externí odkaz:
http://arxiv.org/abs/2404.06076
Autor:
Billfeld, Nir, Kim, Moshe
We develop a novel identification strategy as well as a new estimator for context-dependent causal inference in non-parametric triangular models with non-separable disturbances. Departing from the common practice, our analysis does not rely on the st
Externí odkaz:
http://arxiv.org/abs/2404.05021
We propose Diverse Restormer (DART), a novel image restoration method that effectively integrates information from various sources (long sequences, local and global regions, feature dimensions, and positional dimensions) to address restoration challe
Externí odkaz:
http://arxiv.org/abs/2404.04617