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Robust Deep Ensemble Method for Real-world Image Denoising
[article]
2022
arXiv
pre-print
Preserved Fulltext
In this paper, we propose a simple yet effective Bayesian deep ensemble (BDE) method for real-world image denoising, where several representative deep denoisers pre-trained with various training data settings ...
for improving robustness on real-world noisy images. ...
for improving robustness on real-world noisy images. ...
arXiv:2206.03691v1
fatcat:2ebc2wnaynabphlt33pf4kfdxm
Enhancing the Performance of Convolutional Neural Networks on Quality Degraded Datasets
[article]
2017
arXiv
pre-print
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Abnormal factors, including real-world noise, blur, or other quality degradations, ruin the output of a neural network. ...
Therefore, we present an exhaustive investigation into the effect of noise in image classification and suggest a generalized architecture of a dual-channel model to treat quality degraded input images. ...
In a real-world classification task, a deep neural network often indicates reduced accuracy. The problem is that real-world images often include noise or quality loss. ...
arXiv:1710.06805v1
fatcat:3v4bsrbgynfnbhd6pdsikp3ogi
Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios
[article]
2024
arXiv
pre-print
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2023 pre-print arXiv:2303.16783v1 fatcat:ot5a3jdxczekdhtinz7kizyy7a
Through the analysis of existing methods, we point out that the key to obtaining high-quality and texture-rich results in real-world self-supervised denoising tasks is to train at the original input resolution ...
Self-supervised denoising has attracted widespread attention due to its ability to train without clean images. ...
We train and evaluate our method on two well-known real-world image denoising datasets, Smartphone Image Denoising Dataset (SIDD) [1] and Darmstadt Noise Dataset (DND) [36] . ...
arXiv:2303.16783v2
fatcat:srgxg2lojvgdfpjluh6e3vpw5u
Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling
[article]
2024
arXiv
pre-print
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2024 pre-print arXiv:2307.03992v3 fatcat:4rlajme4gvelpn7nxba7xdofbiOther Versions
Our DMID strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained unconditional diffusion model and an adaptive ensembling method that reduces distortion in the denoised ...
Our DMID strategy achieves state-of-the-art performance on both distortion-based and perception-based metrics, for both Gaussian and real-world image denoising.The code is available at https://github.com ...
Real-world Image Denoising In this section, we conduct real-world image denoising experiments on real-world benchmark datasets. We compare our method with both supervised and unsupervised methods. ...
arXiv:2307.03992v4
fatcat:g7g3ktcuebet3cd5cv2xsqdzwy
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising
2022
IAES International Journal of Artificial Intelligence (IJ-AI)
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Training using transformed images with higher luminance values increases the robustness of the classifier. We have shown that transfer learning is disadvantageous for adversarial machine learning. ...
A nonlocal denoising method with different luminance values has been used to generate adversarial examples from the Modified National Institute of Standards and Technology database (MNIST) and Canadian ...
The verification ensemble then votes on all denoised images. ...
doi:10.11591/ijai.v11.i3.pp961-968
fatcat:npl4i3j2obgo3hcexhxiwkoeyu
RCRN: Real-world Character Image Restoration Network via Skeleton Extraction
[article]
2022
arXiv
pre-print
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Due to the lack of benchmarks for real-world character image restoration, we constructed a dataset containing 1,606 character images with real-world degradation to evaluate the validity of the proposed ...
To address these problems, we propose a real-world character restoration network (RCRN) to effectively restore degraded character images, where character skeleton information and scale-ensemble feature ...
Due to the lack of paired training data for real-world images, GCBD [6] introduces a two-step training method, where a GANbased noise estimator is trained to generate image pairs for training the denoiser ...
arXiv:2207.07795v1
fatcat:fual44j5j5alnddfm25q43iyhy
Confidence-based Reliable Learning under Dual Noises
[article]
2023
arXiv
pre-print
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Experimental results on various challenging synthetic and real-world noisy datasets verify that the proposed method can outperform competing baselines in the aspect of classification performance. ...
Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization. ...
JQ19016), BNRist (BNR2022RC01006), Tsinghua Institute for Guo Qiang, and the High Performance Computing Center, Tsinghua University. J.Z is also supported by the XPlorer Prize. ...
arXiv:2302.05098v1
fatcat:zumpjo22fzdexohqcqvysr2rzq
Masked Pre-trained Model Enables Universal Zero-shot Denoiser
[article]
2024
arXiv
pre-print
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attains the underlying potential for strong image denoising. ...
MPI pre-trains a model with masking and fine-tunes it for denoising of a single image with unseen noise degradation. ...
In essence, our method is adept at real-world denoising, offering a robust solution for image quality enhancement in challenging situations. ...
arXiv:2401.14966v1
fatcat:g3kzbw3r7zae5c5tnqpg7l5jvi
Deep Learning-Based Defect Classification and Detection in SEM Images
[article]
2022
arXiv
pre-print
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2022
Metrology, Inspection, and Process Control XXXVI
unpublished
doi:10.1117/12.2622550 fatcat:iaiaibybabgq3pvdphnnv33nlm
doi:10.1117/12.2622550 fatcat:iaiaibybabgq3pvdphnnv33nlm
We repeated the defect inspection step with the same trained model and performed a comparative analysis for "robustness" and "accuracy" metric with conventional approach for both noisy/denoised image pair ...
This proposes a novel ensemble deep learning-based model to accurately classify, detect and localize different defect categories for aggressive pitches and thin resists (High NA applications).In particular ...
Recurrent usage of these deep learning models can also be observed for robust defect inspection strategy in every single process step of any real-world production or manufacturing pipeline. ...
arXiv:2206.13505v1
fatcat:fhihnbaizbaznlvssb3uvvblji
IDEA-Net: Adaptive Dual Self-Attention Network for Single Image Denoising
2022
2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
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IDEA-Net also shows an appropriate choice to remove real-world noise in low-light and noisy scenes, which in turn, contribute to more accurate dark face detection. ...
The proposed IDEA-Net demonstrated the outperformance on four benchmark datasets compared with other single-image-based learning and nonlearning image denoisers. ...
AWGN) and real-world noisy image denoising. Then, we further measure its practicability of removing real-world noise in low-light and noisy scenes for face detection. ...
doi:10.1109/wacvw54805.2022.00081
fatcat:f3zppsosazg23j2hk2q4zr6piu
Infinite Ensemble for Image Clustering
2016
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16
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clustering and deep clustering methods. ...
clustering for efficient image clustering still remains void. ...
Experimental Settings Data Sets. 13 real-world image data sets with true cluster labels are used for experiments. ...
doi:10.1145/2939672.2939813
dblp:conf/kdd/LiuSLF16
fatcat:uoszx5nz7jhljjfjqskxwduasy
Modeling Generalized Specialist Approach To Train Quality Resilient Snapshot Ensemble
[article]
2022
arXiv
pre-print
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Finally, the experimental analysis on three real-world food and a Malaysian food database showed significant improvement for distorted images with competitive classification performance on pristine food ...
The approach aids the models in the ensemble framework retain general skills of recognizing clean images and shallow skills of classifying noisy images with one deep expertise area on a particular distortion ...
In real-world computer vision applications, images undergo various distortions, such as blur or additive noise during capturing or transmission. ...
arXiv:2206.05853v1
fatcat:xmdxohsevvdmpgpkw22hmscacy
Improved Detection of Adversarial Images Using Deep Neural Networks
[article]
2020
arXiv
pre-print
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Recent studies indicate that machine learning models used for classification tasks are vulnerable to adversarial examples, which limits the usage of applications in the fields with high precision requirements ...
Wiener filter is also introduced as the denoise algorithm to the defense model, which can further improve performance. ...
For the adversarial inputs generated by multiple attack algorithms, which is closer to the real-world cases, 67% overall accuracy can be achieved by applying the wiener filter denoise method. ...
arXiv:2007.05573v1
fatcat:gshqrc6bmvaqtfe2iafcwh2uia
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
[article]
2018
arXiv
pre-print
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This underscores the urgent need for practical defense that can be readily deployed to combat attacks in real-time. ...
The rapidly growing body of research in adversarial machine learning has demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarially generated images. ...
Moreover, research on defense rarely focuses on practicality and scalability, both essential for real-world deployment. ...
arXiv:1802.06816v1
fatcat:sykyoez6trhrtpzybv37sa43eu
Denoising neural networks for magnetic resonance spectroscopy
[article]
2022
arXiv
pre-print
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In this work, we demonstrate that deep learning-based denoising methods can outperform traditional techniques while exhibiting greater robustness to variation in noise and signal characteristics. ...
On both synthetic and experimental data, we show that our deep learning-based approaches can exceed performance of traditional techniques, providing a powerful new class of methods for analysis of scientific ...
Previous approaches for signal denoising in NQR include both time-domain and frequency-domain denoising methods and interference cancellation (see [7] for a review), with some methods leveraging specialized ...
arXiv:2211.00080v1
fatcat:uvtxzdourbaf3plifkmhvpm2uq
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