Enhanced Nonconvex Low-Rank Approximation of Tensor Multi-Modes for Tensor Completion

Zeng, Haijin; Chen, Yongyong; Xie, Xiaozhen; Ning, Jifeng

doi:10.1109/tci.2021.3053699

Cited by 17 publications

(2 citation statements)

References 42 publications

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“…In order to verify the denoising effectiveness of the proposed method, we compare proposed method with BM3D [7], WNNM [13], MLPs [10], TNRD [11], DnCNN [14], EPLL [24], CSF [12], RAMP [57], K-LMS [58], Multiscale [66], Low-rank [74], GSRC [83], Plug-and-play [82], Attention-guided CNN [84], Grouped Multi-Scale [87] and BrN [85]. For a detailed description of the aboved methods, please refer to the introduction and the related work section.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Liu et al [74] applied the coefficient matrix in the lowrank representation (LRR) model to impose total variation (TV) norm constraint, and proposed a new image denoising method. Luo et al [75] defined relative total variation Weighted nuclear norm minimization (WNNM) with total variation (RTV), A relative Total variation and Weighted Nuclear norm minimization (RTV-WNNM) is proposed by applying RTV norm constraint on WNNM low-rank representation model.…”

Section: Low-rank Denoisingmentioning

confidence: 99%

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A Novel Gray Image Denoising Method Using Convolutional Neural Network

Meng

Zhang

2022

IEEE Access

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In order to make the image denoising more effective in high noise level environment, a gray image denoising method based on symmetric dilation residual network is proposed for additive Gaussian white noise. First, the incremental & dilated convolution, BN (Batch Normalization) and leaky ReLU (Rectified Linear Unit) are used to extract and learn features from the input noise image. Then the image is reconstructed by combining the descending & dilated convolution and ReLU. Finally, the effective separation between image and noise is realized by integrating deep residual network and BN. Experimental data of specific denoising model and stochastic denoising model show that this method achieves better objective results based on small resolution input & large resolution input and small dataset & large dataset compared with other methods. A series of subjective visual comparison results also show that proposed method can perform image denoising well in high noise level environment, and there are no problems such as damaged edge or texture details, boundary artifacts and poor definition of the denoised image. The proposed method not only improves the image denoising ability, but also improves the denoising productivity to some extent. In the related work, we have made a comprehensive discussion on the current mainstream image denoising methods based on traditional machine learning and deep learning, which to some extent makes up for the lack of such reviews. In addition, the existing problems and future development direction of image denoising are discussed in detail.

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