Results 21 to 30 of about 6,876,175 (322)
Masked Image Training for Generalizable Deep Image Denoising [PDF]
Computer Vision and Pattern Recognition, 2023 When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method for image denoising, especially with the emergence of Transformer-based ...
Haoyu Chen +7 more
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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising [PDF]
IEEE Transactions on Image Processing, 2016 The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
K. Zhang +4 more
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Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm
Technologies, 2023 Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details.
Rusul Sabah Jebur +4 more
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FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising [PDF]
IEEE Transactions on Image Processing, 2017 Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with ...
K. Zhang, W. Zuo, Lei Zhang
semanticscholar +1 more source
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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Denoising an Image by Denoising Its Curvature Image [PDF]
SIAM Journal on Imaging Sciences, 2014 The first author acknowledges partial support by European Research Council, Starting Grant ref. 306337, and/nby Spanish grants AACC, ref. TIN2011-15954-E, and Plan Nacional, ref. TIN2012-38112. The second author was supported in part by NSF-DMS #0915219.
Marcelo Bertalmío, Stacey Levine
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Self-Supervised Image Denoising for Real-World Images With Context-Aware Transformer [PDF]
IEEE Access, 2023 In recent years, the development of deep learning has been pushing image denoising to a new level. Among them, self-supervised denoising is increasingly popular because it does not require any prior knowledge. Most of the existing self-supervised methods
Dan Zhang, Fangfang Zhou
semanticscholar +1 more source
Multicomponent MR Image Denoising [PDF]
International Journal of Biomedical Imaging, 2009 Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality.
Manjn, José V. +5 more
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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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MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Network [PDF]
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023 Recent advances in deep learning have been pushing image denoising techniques to a new level. In self-supervised image denoising, blind-spot network (BSN) is one of the most common methods.
Dan Zhang +3 more
semanticscholar +1 more source