Results 11 to 20 of about 43,627 (311)
Dual-domain image denoising [PDF]
2013 IEEE International Conference on Image Processing, 2013 Image denoising methods have been implemented in both spatial and transform domains. Each domain has its advantages and shortcomings, which can be complemented by each other. State-of-the-art methods like block-matching 3D filtering (BM3D) therefore combine both domains. However, implementation of such methods is not trivial.
Claude Knaus, Matthias Zwicker
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Image Denoising with Directional Bases [PDF]
2007 IEEE International Conference on Image Processing, 2007 Directional information is an important component of both natural and synthetic images, and it is exploited in many image processing applications. Directional basis analysis is used to capture significant structural information. This paper presents an empirical study of image denoising with directional bases. We consider two distinct approaches.
Heechan Park, Graham R. Martin, Zhen Yao
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Flashlight CNN Image Denoising [PDF]
2020 28th European Signal Processing Conference (EUSIPCO), 2021 This paper proposes a learning-based denoising method called FlashLight CNN (FLCNN) that implements a deep neural network for image denoising. The proposed approach is based on deep residual networks and inception networks and it is able to leverage many more parameters than residual networks alone for denoising grayscale images corrupted by additive ...
Pham Huu Thanh Binh +2 more
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Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
Applied Sciences, 2023 In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition.
Roopdeep Kaur +2 more
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Image Denoising Using Framelet Transform [PDF]
Engineering and Technology Journal, 2010 In many of the digital image processing applications, observed image ismodeled to be corrupted by different types of noise that result in a noisy version.Hence image denoising is an important problem that aims to find an estimateversion from noisy image ...
Ali K. Nahar, Hadeel N. Abduallah
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A Triple Deep Image Prior Model for Image Denoising Based on Mixed Priors and Noise Learning
Applied Sciences, 2023 Image denoising poses a significant challenge in computer vision due to the high-level visual task’s dependency on image quality. Several advanced denoising models have been proposed in recent decades. Recently, deep image prior (DIP), using a particular
Yong Hu +4 more
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Image denoising algorithm of social network based on multifeature fusion
Journal of Intelligent Systems, 2022 A social network image denoising algorithm based on multifeature fusion is proposed. Based on the multifeature fusion theory, the process of social network image denoising is regarded as the fitting process of neural network, and a simple and efficient ...
Zhao Lanfei, Zhu Qidan
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Image Denoising With Generative Adversarial Networks and its Application to Cell Image Enhancement
IEEE Access, 2020 This paper proposes an image denoising training framework based on Wasserstein Generative Adversarial Networks (WGAN) and applies it to cell image denoising. Cell image denoising is a challenging task which has high requirement on the recovery of feature
Songkui Chen +3 more
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Non-local clustering via sparse prior for sports image denoising
EAI Endorsed Transactions on Scalable Information Systems, 2022 This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173794. Image denoising is very important in image preprocessing. In order to introduce the priori information of external clean image
Ying Zhang
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Joint Image Denoising with Gradient Direction and Edge-Preserving Regularization [PDF]
, 2021 Joint image denoising algorithms use the structures of the guidance image as a prior to restore the noisy target image. While the provided guidance images are helpful to improve the denoising performance, the denoised edges are most likely to be blurred ...
Li, Pengliang +4 more
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