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High Order Total Variational Denoising Algorithm Based on lOverlapping Combination Sparse

Instrumentation 2024年 30-40页

作者： Binxin Tang Xianchun Zhou Chengcheng Cui Yang Rui School of Electronic and Information Engineering Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing University of Information Science and Technology

For addressing impulse noise in images,this paper proposes a denoising algorithm for non-convex impulse noise images based on the l0norm fidelity *** the total variation of the l0norm has a better denoising effect on the pulse noise,it is chosen as the model fidelity term,and the overlapping group sparse term combined with non-convex higher term is used as the regularization term of the model to protect the image edge texture and suppress the staircase *** the same time,the alternating direction method of multipliers,the majorizationminimization method and the mathematical program with equilibrium constraints were used to solve the *** results show that the proposed model can effectively suppress the staircase effect in smooth regions,protect the image edge details,and perform better in terms of the peak signal-to-noise ratio and the structural similarity index measure.

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Image Denoising Using Dual Convolutional Neural Network with Skip Connection

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Instrumentation 2024年 74-85页

作者： Mengnan Lü Xianchun Zhou Zhiting Du Yuze Chen Binxin Tang School of Electronics Information Engineering Nanjing University of Information Science & Technology School of Artificial Intelligence Nanjing University of Information Science and Technology

In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training costs and long inference times, limiting their practical application in denoising tasks. This paper proposes a new dual convolutional denoising network with skip connections(DECDNet), which achieves an ideal balance between denoising effect and network complexity. The proposed DECDNet consists of a noise estimation network, a multi-scale feature extraction network, a dual convolutional neural network, and dual attention *** noise estimation network is used to estimate the noise level map, and the multi-scale feature extraction network is combined to improve the model's flexibility in obtaining image features. The dual convolutional neural network branch design includes convolution and dilated convolution interactive connections, with the lower branch consisting of dilated convolution layers, and both branches using skip connections. Experiments show that compared with other models, the proposed DECDNet achieves superior PSNR and SSIM values at all compared noise levels,especially at higher noise levels, showing robustness to images with higher noise levels. It also demonstrates better visual effects, maintaining a balance between denoising and detail preservation.

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Multi-Modal Medical Image Fusion Based on Improved Parameter Adaptive PCNN and Latent Low-Rank Representation

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Instrumentation 2024年第2期11卷 53-63页

作者： Zirui Tang Xianchun Zhou School of Artificial Intelligence(School of Future Technology) Nanjing University of Information Science&TechnologyNanjing 210000China

Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.

关键词： image fusion improved parameter adaptive pcnn non-subsampled shear-wave transform latent low-rank representation

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Multi-scale Attention Dilated Residual Image Denoising Network Based on Skip Connection

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Instrumentation 2024年 41-53页

作者： Zhiting Du Xianchun Zhou Mengnan Lü Yuze Chen Binxin Tang School of Electronics Information Engineering Nanjing University of Information Science & Technology

In the field of image denoising, deep learning technology holds a dominance. However,the current network model tends to lose fine-grained information with the depth of the *** address this issue, this paper proposes a Multi-scale Attention Dilated Residual Image Denoising Network(MADRNet) based on skip connection, which consists of Dense Interval Transmission Block(DTB), Sparse Residual Block(SRB), Dilated Residual Attention Reconstruction Block(DRAB) and Noise Extraction Block(NEB). The DTB enhances the classical dense layer by reducing information redundancy and extracting more accurate feature information. Meanwhile,SRB improves feature information exchange and model generalization through the use of sparse mechanism and skip connection strategy with different expansion factors. The NEB is primarily responsible for extracting and estimating noise. Its output, together with that of the sparse residual module, acts on the DRAB to effectively prevent loss of shallow feature information and improve denoising effect. Furthermore, the DRAB integrates an dilated residual block into an attention mechanism to extract hidden noise information while using residual learning technology to reconstruct clear images. We respectively examined the performance of MADRNet in gray image denoising, color image denoising and real image denoising. The experiment results demonstrate that proposed network outperforms some excellent image denoising network in terms of peak signal-to-noise ratio, structural similarity index measurement and denoising *** proposed network effectively addresses issues associated with the loss of detail information.

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