scholarly journals Digital model creation and image meticulous processing based on variational partial differential equation

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Pei Wang ◽  
Hani Jamal Sulaimani ◽  
Sae Hoon Kim
Keyword(s):  
Differential Equation ◽  
Image Denoising ◽  
Digital Model ◽  
Original Image ◽  
Application Research ◽  
Partial Differential ◽  
Practical Operation ◽  
Noise Image ◽  
Constraint Information ◽  
Basic Content

Abstract In the application of digital model of animation scene, image restoration technology and image denoising technology are the basic tasks of practical operation, which are closely related, but there exist also essential differences. The reason is that both of them want to obtain the original image from the degraded noise image or damaged image, but generally speaking, as there is no sufficient constraint information to accurately recover the original image, both of them are unwell-posed inverse problems. Therefore, on the basis of understanding the basic content and application research status of variational partial differential equations (PDEs), this paper discusses the application value of variational PDEs in image denoising and restoration according to the image processing requirements in the digital model of animation scenes.

scholarly journals Patch Similarity Modulus and Difference Curvature Based Fourth-Order Partial Differential Equation for Image Denoising

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Yunjiao Bai ◽  
Quan Zhang ◽  
Hong Shangguan ◽  
Zhiguo Gui ◽  
Yi Liu ◽  
...  
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Image Denoising ◽  
Fourth Order ◽  
Objective Evaluation ◽  
Order Partial Differential Equation ◽  
Partial Differential ◽  
The Difference ◽  
Adaptively Adjusting ◽  
Difference Curvature

The traditional fourth-order nonlinear diffusion denoising model suffers the isolated speckles and the loss of fine details in the processed image. For this reason, a new fourth-order partial differential equation based on the patch similarity modulus and the difference curvature is proposed for image denoising. First, based on the intensity similarity of neighbor pixels, this paper presents a new edge indicator called patch similarity modulus, which is strongly robust to noise. Furthermore, the difference curvature which can effectively distinguish between edges and noise is incorporated into the denoising algorithm to determine the diffusion process by adaptively adjusting the size of the diffusion coefficient. The experimental results show that the proposed algorithm can not only preserve edges and texture details, but also avoid isolated speckles and staircase effect while filtering out noise. And the proposed algorithm has a better performance for the images with abundant details. Additionally, the subjective visual quality and objective evaluation index of the denoised image obtained by the proposed algorithm are higher than the ones from the related methods.

scholarly journals A New Image Denoising Method by Combining WT with ICA

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chengzhi Ruan ◽  
Dean Zhao ◽  
Weikuan Jia ◽  
Chen Chen ◽  
Yu Chen ◽  
...  
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Median Filtering ◽  
Noisy Image ◽  
Original Image ◽  
Signal To Noise ◽  
Denoising Method ◽  
Noise Image ◽  
Solid Foundation ◽  
Lower Noise

In order to improve the image denoising ability, the wavelet transform (WT) and independent component analysis (ICA) are both introduced into image denoising in this paper. Although these two algorithms have their own advantages in image denoising, they are unable to reduce noises completely, which makes it difficult to achieve ideal effect. Therefore, a new image denoising method is proposed based on the combination of WT with ICA (WT-ICA). For verifying the WT-ICA denoising method, we adopt four image denoising methods for comparison: median filtering (MF), wavelet soft thresholding (WST), ICA, and WT-ICA. From the experimental results, it is shown that WT-ICA can significantly reduce noises and get lower-noise image. Moreover, the average of WT-ICA denoising image’s peak signal to noise ratio (PSNR) is improved by 20.54% compared with noisy image and 11.68% compared with the classical WST denoising image, which demonstrates its advantage. From the performance of texture and edge detection, denoising image by WT-ICA is closer to the original image. Therefore, the new method has its unique advantage in image denoising, which lays a solid foundation for the realization of further image processing task.

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