Weighted Non-Linear Diffusion Filtering with Wavelet Thresholding in Image Denoising

In order to improve the effect of face image denoising, this paper put forward several face image denoising methods based on partial differential equations, including P-M non-linear diffusion equations and fourth-order partial differential equations. We use those two methods by establishing non-linear diffusion equations and fourth-order anisotropic diffusion partial differential equation. The P-M non-linear diffusion denoising method can remove noise in intra-regions sufficiently but noise at edges can not be eliminated successfully and line-like structures can not be held very well.While the fourth-order partial differential equations denoising can retain the local detail characteristics of the original face image. Finally, through the experimental results we can see the effect of the fourth-order partial differential equations denoising is better, which makes the later face image processing more accurate and promotes the development of face image processing.

Download Full-text

Adaptive Non-linear Diffusion Based Local Binary Pattern for Image Denoising

2018 International Conference on Applied Smart Systems (ICASS) ◽

10.1109/icass.2018.8652010 ◽

2018 ◽

Cited By ~ 1

Author(s):

Azizi Abdallah ◽

Azizi Zineb

Keyword(s):

Image Denoising ◽

Local Binary Pattern ◽

Linear Diffusion ◽

Non Linear

Download Full-text

Image Forgery Detection using AKAZE Keypoint Feature Extraction and Trie Matching

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4784.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 2208-2213

Keyword(s):

Data Structure ◽

Scale Space ◽

Linear Diffusion ◽

Image Forgery ◽

Illegal Activity ◽

Detection Techniques ◽

Diffusion Filtering ◽

Key Points ◽

Non Linear ◽

Image Integrity

Image Forgery is an illegal activity in the society as per cyber laws. There are various types of forgeries in which forgery on images is considered as an illegal activity. Image forgery may take place in different ways. One way for doing forgery on images is copy and move forgery which may result in loss of image integrity or authenticity. There are number of popular detection techniques exist such as SIFT, SURF etc., but have high complexity in detection of forgery. Here we have proposed a method to detect the forgery on images which results in loss of integrity or authenticity. In our proposed method we have used descriptor matching using Trie Data Structure The descriptor matching method of implementation using Trie data structure made the complexity of the problem to reduce to O (n log n). Using Key points approach we can verify the integrity of the image. Extracting the features with key points approach is computational expensive task. But there is KAZE method which overcomes this situation. KAZE’s method of using non-linear diffusion filtering requires it to solve a series of PDEs. This cannot be done analytically forcing KAZE to use a numerical method called an AOS scheme to solve the PDEs. However, this process is computationally costly and therefore an accelerated version of KAZE was created. The Accelerated KAZE or AKAZE which creates non-linear scale space through Fast Explicit Diffusion for reduce the complexity in extracting the features.

Download Full-text