Abstract
The system introduces the extensive application and development process of image denoising based on non-local mean. The principle and specific theoretical model of the non-local mean algorithm are described. The process of improving the non-local mean algorithm after being proposed and how to improve it is elaborated and the shortcomings of the algorithm are pointed out. The noise reduction algorithm is experimentally described in detail from the aspects of peak signal-to-noise ratio, mean square error and structural similarity under different noise environment conditions.
Highlights
With the rapid development of computer science, the role of digital image processing technology in daily life has become increasingly prominent
Image processing technology has been widely used in daily life and scientific research
Since the existence of these noises reduces the quality of the image and affects the correct recognition and understanding of the image information by the staff, the importance of image noise reduction in image processing technology can be known
Summary
With the rapid development of computer science, the role of digital image processing technology in daily life has become increasingly prominent. There are three main types of noise: Gaussian white noise, which is generated by electronic devices; Poisson noise, mainly generated in the process of photoelectric conversion whose influence of noise is more obvious in the case of weak light This kind of noise is modeled by random variables of Poisson distribution; Speckle noise, the SAR imaging system A major feature is the signal-related small spots on the image which reduces the image quality of the image and seriously affects the automatic segmentation, classification, target detection and other thematic information extraction. Non-local mean (NLM) filtering algorithm [6], the basic idea is that the ultrasound image contains a lot of redundant information, as there are many very similar image blocks, but these similar image blocks are distributed over the whole image. The similarity between these similar blocks and the image block where the current noise is located is calculated, and secondly the weighted average is used to recover the value of the pixel to be restored. [7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
