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IEEE Transactions on Geoscience and Remote Sensing, 2022
Hyperspectral image (HSI) denoising is a fundamental task in remote sensing image processing, which is helpful for HSI subsequent applications, such as unmixing and classification.
Wei-Hao Wu +4 more
semanticscholar +1 more source
Hyperspectral image (HSI) denoising is a fundamental task in remote sensing image processing, which is helpful for HSI subsequent applications, such as unmixing and classification.
Wei-Hao Wu +4 more
semanticscholar +1 more source
Improved Denoising Auto-Encoders for Image Denoising
2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018Image denoising is an important pre-processing step in image analysis. Various denoising algorithms, such as BM3D, PCD and K-SVD, obtain remarkable effects. Recently a deep denoising auto-encoder has been proposed and shown excellent performance compared to conventional image denoising algorithms.
Qian Xiang, Xuliang Pang
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Semi-Supervised DAS VSP Data Denoising Using Signal and Noise Distribution Difference
IEEE Transactions on Geoscience and Remote SensingDistributed acoustic sensing (DAS), an emerging technology for signal acquisition, has been progressively applied to collect vertical seismic profile (VSP) data.
Man Zhang +4 more
semanticscholar +1 more source
Dynamic Slimmable Denoising Network
IEEE Transactions on Image Processing, 2023Recently, tremendous human-designed and automatically searched neural networks have been applied to image denoising. However, previous works intend to handle all noisy images in a pre-defined static network architecture, which inevitably leads to high computational complexity for good denoising quality.
Zutao Jiang +5 more
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A High-Quality Denoising Dataset for Smartphone Cameras
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018The last decade has seen an astronomical shift from imaging with DSLR and point-and-shoot cameras to imaging with smartphone cameras. Due to the small aperture and sensor size, smartphone images have notably more noise than their DSLR counterparts. While
A. Abdelhamed, Stephen Lin, M. S. Brown
semanticscholar +1 more source
Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image
Computer Vision and Pattern Recognition, 2020In last few years, supervised deep learning has emerged as one powerful tool for image denoising, which trains a denoising network over an external dataset of noisy/clean image pairs. However, the requirement on a high-quality training dataset limits the
Yuhui Quan +3 more
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Deconstructing Denoising Diffusion Models for Self-Supervised Learning
International Conference on Learning RepresentationsIn this study, we examine the representation learning abilities of Denoising Diffusion Models (DDM) that were originally purposed for image generation.
Xinlei Chen +3 more
semanticscholar +1 more source
2007
We consider the problem of denoising a noisily sampled submanifold M in R^d, where the submanifold M is a priori unknown and we are only given a noisy point sample. The presented denoising algorithm is based on a graph-based diffusion process of the point sample.
Hein, M., Maier, M.
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We consider the problem of denoising a noisily sampled submanifold M in R^d, where the submanifold M is a priori unknown and we are only given a noisy point sample. The presented denoising algorithm is based on a graph-based diffusion process of the point sample.
Hein, M., Maier, M.
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Multiscale Image Blind Denoising
IEEE Transactions on Image Processing, 2015Arguably several thousands papers are dedicated to image denoising. Most papers assume a fixed noise model, mainly white Gaussian or Poissonian. This assumption is only valid for raw images. Yet, in most images handled by the public and even by scientists, the noise model is imperfectly known or unknown.
Marc, Lebrun +2 more
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U2D2Net: Unsupervised Unified Image Dehazing and Denoising Network for Single Hazy Image Enhancement
IEEE transactions on multimediaHazy images captured under ill-posed scenarios with scattering medium (i.e. haze, fog, or smoke) are contaminated in visibility. Inevitably, these images are further degraded by noises owing to real-world imaging.
Bosheng Ding +6 more
semanticscholar +1 more source