Results 41 to 50 of about 18,244,520 (347)
Locally Adaptive Channel Attention-Based Network for Denoising Images
IEEE Access, 2020 Channel attention has recently been proposed and shown a great improvement in image classification accuracy. In this paper, we show that channel attention can greatly help a low-level vision task, image denoising, as well, and propose channel attention ...
Haeyun Lee, Sunghyun Cho
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Multispinning for Image Denoising
Journal of Intelligent Systems, 2012 Abstract. The problem of reconstructing digital images from degraded measurements is regarded as a problem of importance in various fields of engineering and imaging science. The main goal of denoising is to restore a noisy image to produce a visually high quality image.
B. N. Aravind, K. V. Suresh 0001
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A multiresolution framework for local similarity based image denoising [PDF]
, 2012 In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity.
Rajpoot, Nasir M. (Nasir Mahmood) +1 more
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SUNet: Swin Transformer UNet for Image Denoising [PDF]
International Symposium on Circuits and Systems, 2022 Image restoration is a challenging ill-posed problem which also has been a long-standing issue. In the past few years, the convolution neural networks (CNNs) almost dominated the computer vision and had achieved considerable success in different levels ...
Chi-Mao Fan +2 more
semanticscholar +1 more source
DCT Image Denoising: a Simple and Effective Image Denoising Algorithm [PDF]
Image Processing On Line, 2011 This work presents a simple but effective denoising algorithm using a local DCT thresholding. This thresholding is applied separately to each color channel after decorrelation. Due to its simplicity and excellent performance, this contribution can be considered as a baseline for comparison and lower bound of performance for newly developed techniques.
Guoshen Yu, Guillermo Sapiro
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Diffusion Model for Generative Image Denoising [PDF]
arXiv.org, 2023 In supervised learning for image denoising, usually the paired clean images and noisy images are collected or synthesised to train a denoising model. L2 norm loss or other distance functions are used as the objective function for training. It often leads
Yutong Xie +3 more
semanticscholar +1 more source
WINNet: wavelet-inspired invertible network for image denoising [PDF]
, 2021 Image denoising aims to restore a clean image from an observed noisy one. Model-based image denoising approaches can achieve good generalization ability over different noise levels and are with high interpretability. Learning-based approaches are able to
Huang, Jun-Jie, Dragotti, Pier Luigi
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Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising [PDF]
IEEE Geoscience and Remote Sensing LettersHyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. However, simultaneously modeling global and local features is rarely explored to enhance HSI denoising.
Shuai Hu +4 more
semanticscholar +1 more source
Parallel Implementation of Wavelet-based Image Denoising on Programmable PC-grade Graphics Hardware [PDF]
, 2010 The Discrete Wavelet Transform (DWT) has been extensively used for image compression and denoising in the areas of image processing and computer vision. However, the intensive computation of DWT due to its multilevel data decomposition and reconstruction
Xu, Zhijie, Su, Yang
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Edge-aware image denoising algorithm
Journal of Algorithms & Computational Technology, 2018 The key of image denoising algorithms is to preserve the details of the original image while denoising the noise in the image. The existing algorithms use the external information to better preserve the details of the image, but the use of external ...
Xiangning Zhang, Yan Yang, Lening Lin
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