A new blind image denoising method based on asymmetric generative adversarial network

Wang, Yiming; Chang, Dongxia; Zhao, Yao

doi:10.1049/ipr2.12102

Cited by 17 publications

(4 citation statements)

References 33 publications

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“…Wang Y. and other scholars proposed a blind ID method in view of asymmetric generative adversarial networks. The outcomes showcase that this method has good denoising effect and robustness [14]. Shi K. et al presented an ID method in view of local and nonlocal regularization fourth-order evolution equations.…”

Section: Related Workmentioning

confidence: 87%

Enhanced Image Denoising with Diffusion Probability and Dictionary Learning Adaptation

2024

Teh. vjesn.

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Image denoising is essential for numerous image processing applications, where image noise can profoundly impact processing efficiency and output quality. Addressing the challenge of inflexible reference images in unconditional diffusion probability models and enhancing image denoising performance is of paramount importance. In this research, we propose a novel image denoising model based on component decoupling and introduce sensitivity decoupling operators to prevent entanglement and redundancy among different decoupling models. Additionally, we leverage a model-driven network to fuse image components, resisting noise and model degradation, thereby aiding network convergence. Subsequently, we construct an image adaptive denoising model incorporating diffusion probability and dictionary learning. Experimental results demonstrate the superiority of the proposed approach over other algorithms in grayscale image processing on the Set12 dataset, achieving a peak signal-to-noise ratio (PSNR) of 35.75 dB and an average structural similarity (SSIM) value of 92.68%. Similarly, on the BSD68 dataset, our algorithm outperforms others with a PSNR of 34.35 dB and an average SSIM of 93.89%. Furthermore, for colour image processing, our method yields higher PSNR and average SSIM compared to other approaches. The findings indicate a significant improvement in denoising effectiveness compared to prior methods, highlighting the practical value of the proposed image denoising algorithm.

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Section: Related Workmentioning

confidence: 87%

Enhanced Image Denoising with Diffusion Probability and Dictionary Learning Adaptation

2024

Teh. vjesn.

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“…This method added an image downsampling layer in the generative model and the discriminative model, and it utilized a multi-scale feature downsampling layer to extract image features to reduce the impact of noise on the training image. The research results indicated that the performance was verified, with high performance and flexibility [14]. Wang P et al…”

Section: Introductionmentioning

confidence: 84%

Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

Fang,

Hui,

P. Rey

et al. 2024

JAIT

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Historical architecture is an important carrier of cultural and historical heritage in a country and region, and its protection and restoration work plays a crucial role in the inheritance of cultural heritage. However, the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors. Therefore, an artificial intelligence repair technology based on three-dimensional (3D) point cloud reconstruction and generative adversarial networks was proposed to improve the precision and efficiency of repair work. Firstly, in-depth research on the principles and algorithms of 3D point cloud data processing and generative adversarial networks should be conducted. Secondly, a digital restoration framework was constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes. The experimental results showed that the errors in the restoration of palace buildings, defense walls, pagodas, altars, temples, and mausoleums were 0.17, 0.12, 0.13, 0.11, and 0.09, respectively. The technique can significantly reduce the error while maintaining the high precision repair effect. This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration. It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.

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“…In the past few decades, model‐based restoration methods have been widely studied [1–23]. Recently, deep learning techniques have been introduced in image restoration, which achieve state‐of‐the‐art restoration performance [24–31]. In fact, the frameworks of many deep learning‐based restoration methods have some strong correlations with model‐based methods [32–34], and training a satisfactory deep restoration network is a non‐trivial task.…”

Section: Introductionmentioning

confidence: 99%

Image restoration via exponential scale mixture‐based simultaneous sparse prior

Yuan

Liu

Liang

2022

IET Image Processing

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Image prior plays a decisive role in the performance of widely studied model‐based restoration methods. To further improve restoration performance, this paper proposes an exponential scale mixture‐based simultaneous sparse prior (ESM‐SSP) to accurately characterize image prior information. Specifically, first, two structured dictionaries are adaptively learned to explore the local and non‐local sparsity of similar patch groups simultaneously. Then, the exponential scale mixture (ESM) is employed to model simultaneous sparse coefficients. The adoption of ESM enables us to accurately estimate simultaneous sparse coefficients by adaptively adjusting the regularization parameters. With the aid of ESM‐SSP, an effective image restoration algorithm is developed to preserve more image details. Extensive experimental results on image denoising and deblocking demonstrate that compared with many state‐of‐the‐art model‐based methods, the proposed ESM‐SSP‐based restoration algorithm not only has competitive peak signal‐to‐noise ratio, but also produces higher structural similarity index and better visuals. More importantly, the proposed method can also compete favourably with the superior deep learning‐based restoration methods.

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A new blind image denoising method based on asymmetric generative adversarial network

Cited by 17 publications

References 33 publications

Enhanced Image Denoising with Diffusion Probability and Dictionary Learning Adaptation

Enhanced Image Denoising with Diffusion Probability and Dictionary Learning Adaptation

Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

Image restoration via exponential scale mixture‐based simultaneous sparse prior

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