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

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 traditional ideal lowpass filter. In… 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 >