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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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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
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A cross Transformer for image denoising [PDF]
Information Fusion, 2023 Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for ...
Chunwei Tian +5 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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Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising [PDF]
Computer Vision and Pattern Recognition, 2023 Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing the great power of deep learning, existing HSI denoising methods suffer from limitations in capturing the nonlocal self-similarity.
Miaoyu Li +4 more
semanticscholar +1 more source
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 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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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
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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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EWT: Efficient Wavelet-Transformer for Single Image Denoising [PDF]
Neural Networks, 2023 Transformer-based image denoising methods have shown remarkable potential but suffer from high computational cost and large memory footprint due to their linear operations for capturing long-range dependencies.
Juncheng Li +4 more
semanticscholar +1 more source