Yibin Tang¹, Ying Chen², Aimin Jiang¹, Jian Li¹, Yan Zhou^1,*, Hon Keung Kwan³

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 67-80, 2021, DOI:10.32604/cmc.2021.017300 - 04 June 2021

Abstract Graph filtering is an important part of graph signal processing and a useful tool for image denoising. Existing graph filtering methods, such as adaptive weighted graph filtering (AWGF), focus on coefficient shrinkage strategies in a graph-frequency domain. However, they seldom consider the image attributes in their graph-filtering procedure. Consequently, the denoising performance of graph filtering is barely comparable with that of other state-of-the-art denoising methods. To fully exploit the image attributes, we propose a guided intra-patch smoothing AWGF (AWGF-GPS) method for single-image denoising. Unlike AWGF, which employs graph topology on patches, AWGF-GPS learns the topology… More >

Peizhu Gong, Jin Liu^*, Shiqi Lv

Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 155-163, 2020, DOI:10.32604/jihpp.2020.010453 - 07 January 2021

Abstract Image denoising is often used as a preprocessing step in computer vision tasks, which can help improve the accuracy of image processing models. Due to the imperfection of imaging systems, transmission media and recording equipment, digital images are often contaminated with various noises during their formation, which troubles the visual effects and even hinders people’s normal recognition. The pollution of noise directly affects the processing of image edge detection, feature extraction, pattern recognition, etc., making it difficult for people to break through the bottleneck by modifying the model. Many traditional filtering methods have shown poor… More >

Ke Jin, Shunfeng Wang^*

Journal on Internet of Things, Vol.2, No.1, pp. 1-11, 2020, DOI:10.32604/jiot.2020.09071 - 06 August 2020

Abstract In recent years, image restoration has become a huge subject, and finite hybrid model has been widely used in image denoising because of its easy modeling and strong explanatory results. The gaussian mixture model is the most common one. The existing image denoising methods usually assume that each component of the natural image is subject to the gaussian mixture model (GMM). However, this approach is not entirely reasonable. It is well known that most natural images are complex and their distribution is not entirely gaussian. As a result, there are still many problems that GMM More >

Jianwei Zhang¹, Ze Qin¹, Shunfeng Wang^{1, *}

Journal of Cyber Security, Vol.2, No.1, pp. 25-36, 2020, DOI:10.32604/jcs.2020.06429

Abstract Digital images have been applied to various areas such as evidence in courts. However, it always suffers from noise by criminals. This type of computer network security has become a hot issue that can’t be ignored. In this paper, we focus on noise removal so as to provide guarantees for computer network security. Firstly, we introduce a well-known denoising method called Expected Patch Log Likelihood (EPLL) with Gaussian Mixture Model as its prior. This method achieves exciting results in noise removal. However, there remain problems to be solved such as preserving the edge and meaningful… More >

Ying Chen^{1, 2}, Yibin Tang³, Lin Zhou¹, Yan Zhou^{3, 4}, Jinxiu Zhu³, Li Zhao^{1, *}

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1219-1232, 2020, DOI:10.32604/cmc.2020.010638 - 10 June 2020

Abstract Graph filtering, which is founded on the theory of graph signal processing, is proved as a useful tool for image denoising. Most graph filtering methods focus on learning an ideal lowpass filter to remove noise, where clean images are restored from noisy ones by retaining the image components in low graph frequency bands. However, this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective. To address this issue, we propose an adaptive weighted graph filtering (AWGF) method to replace the design of… More >

Xiaoli Li^1,*, Chao Ye¹, Yujia Yan², Zhenlong Du¹

Journal of New Media, Vol.1, No.2, pp. 75-85, 2019, DOI:10.32604/jnm.2019.06259

Abstract In order to improve the quality of low-dose computational tomography (CT) images, the paper proposes an improved image denoising approach based on WGAN-gp with Wasserstein distance. For improving the training and the convergence efficiency, the given method introduces the gradient penalty term to WGAN network. The novel perceptual loss is introduced to make the texture information of the low-dose images sensitive to the diagnostician eye. The experimental results show that compared with the state-of-art methods, the time complexity is reduced, and the visual quality of low-dose CT images is significantly improved. More >