Application and optimization of wavelet threshold denoising algorithm in signal processing

Wavelet threshold denoising algorithm is a non parametric method. The image without changing its original form. Wavelet threshold denoising algorithm is also known as wavelet based denoising (WBD) or wavelet transform based denoising (wtbd). The application of wavelet in signal processing is to denoise the signal. It is used to reduce noise and improve the quality of digital image or video. This technology can reduce the background noise without affecting any other information in the image, and can be applied to video compression, image processing, medical imaging and other fields. Wavelet is a kind of mathematical function, which can be applied to image, audio signal and video sequence. Wavelet transform decomposes the image into its components (wavelet). Each part has a specific frequency content and scale factor, which allows reconstruction at any point on the time axis of the image sequence without losing resolution. Compared with other technologies, the main advantage of using this technology is that it will not change the original signal, but only remove the noise in it, so that you can clearly understand the content displayed in your application.