Abstract
Aiming at the problem of unclear images acquired in interactive systems, an improved image processing algorithm for nonlocal mean denoising is proposed. This algorithm combines the adaptive median filter algorithm with the traditional nonlocal mean algorithm, first adjusts the image window adaptively, selects the corresponding pixel weight, and then denoises the image, which can have a good filtering effect on the mixed noise. The experimental results show that, compared with the traditional nonlocal mean algorithm, the algorithm proposed in this paper has better results in the visual quality and peak signal-to-noise ratio (PSNR) of complex noise images.
Highlights
Use median value instead of gray value(1) e improved Nonlocal mean (NLM) filtering algorithm proposed in this paper has excellent performance in both mixed noise removal and single noise removal
Nonlocal mean (NLM) algorithm is an excellent spatial denoising algorithm [14,15,16]. e algorithm uses the characteristics of many similar blocks in the image and the average value of similar blocks to suppress image noise
The principle of NLM algorithm is simple and the denoising effect is obvious, the calculation is not simple. e main reason is that the calculation of the similarity weight is complicated and takes a lot of calculation time. erefore, how to remove pixels with little correlation while maintaining the noise reduction effect to reduce the amount of calculation will bring practical performance improvements to the NLM algorithm. e NLM algorithm has a weak filtering ability for salt and pepper noise
Summary
(1) e improved NLM filtering algorithm proposed in this paper has excellent performance in both mixed noise removal and single noise removal Both the visual effect and the PSNR have reached a good level (a)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
