Blind Image Denoising via Dynamic Dual Learning

Du, Yong; Han, Guoqiang; Tan, Yushan; Xiao, Chufeng; He, Shengfeng

doi:10.1109/tmm.2020.3008057

Cited by 16 publications

(3 citation statements)

References 45 publications

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“…For instance, Song et al [21] combined dynamic convolutions and residual learning operations into a CNN to dynamically adjust parameters to obtain a robust denoising network, according to different input images. Du et al [22] exploited a dynamic attention mechanism to better extract salient information for image denoising. Alternatively, Shen et al [23] fused a spatial module and dynamic convolution to obtain more spatial context information to obtain better denoising performance.…”

Section: Dynamic Network For Image Denoisingmentioning

confidence: 99%

Lightweight Image Denoising Network for Multimedia Teaching System

Zhang,

Tian,

Zhang

et al. 2023

Mathematics

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Due to COVID-19, online education has become an important tool for teachers to teach students. Also, teachers depend on a multimedia teaching system (platform) to finish online education. However, interacted images from a multimedia teaching system may suffer from noise. To address this issue, we propose a lightweight image denoising network (LIDNet) for multimedia teaching systems. A parallel network can be used to mine complementary information. To achieve an adaptive CNN, an omni-dimensional dynamic convolution fused into an upper network can automatically adjust parameters to achieve a robust CNN, according to different input noisy images. That also enlarges the difference in network architecture, which can improve the denoising effect. To refine obtained structural information, a serial network is set behind a parallel network. To extract more salient information, an adaptively parametric rectifier linear unit composed of an attention mechanism and a ReLU is used into LIDNet. Experiments show that our proposed method is effective in image denoising, which can also provide assistance for multimedia teaching systems.

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Section: Dynamic Network For Image Denoisingmentioning

confidence: 99%

Lightweight Image Denoising Network for Multimedia Teaching System

Zhang,

Tian,

Zhang

et al. 2023

Mathematics

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“…(8) and the λ HF controls the weight of the high frequency loss Eq. (9). In order to find the most suitable hyper-parameters, we designed following experiments.…”

Section: Ablation Analysismentioning

confidence: 99%

“…Recently, deep learning technology drives the development of image restoration tasks [7], [8], [9], [10], [11]. There are lots of learning-based deblurring methods that have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Grid Network for Image Deblurring With High-Frequency Guidance

Liu

Fang

Wang

et al. 2022

IEEE Trans. Multimedia

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It has been demonstrated that the blurring process reduces the high-frequency information of the original sharp image, so the main challenge for image deblurring is to reconstruct high-frequency information from the blurry image. In this paper, we propose a novel image deblurring framework to focus on the reconstruction of high-frequency information, which consists of two main subnetworks: a high-frequency reconstruction subnetwork (HFRSN) and a multi-scale grid subnetwork (MSGSN). The HFRSN is built to reconstruct latent high-frequency information from multiple scale blurry images. The MSGSN performs deblurring processes with high-frequency guidance at different scales simultaneously. Besides, in order to better use high-frequency information to restore sharpening images, we designed a high-frequency information aggregation (HFAG) module and a high-frequency information attention (HFAT) module in MSGSN. The HFAG module is designed to fuse high-frequency features and image features at the feature extraction stage, and the HFAT module is built to enhance the feature reconstruction stage. Extensive experiments on different datasets show the effectiveness and efficiency of our method.

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