Research on Image Denoising Method Based on Wavelet Transform

In the process of image acquisition and transmission, the image always generates noise due to internal and external interference. Noise reduces the quality of the image, and makes it difficult for subsequent image processing. Therefore, image denoising is very important in image processing. Wavelet denoising can effectively filter out noise and retain high-frequency information of the image, this method has the characteristics of fast operation speed and has become an important branch of image denoising. Threshold functions commonly used in wavelet threshold denoising include hard threshold function and soft threshold function. The hard threshold function is not continuous as a whole. Although the soft threshold function has good continuity, there is always a constant deviation between the processed coefficient and the original coefficient when the wavelet coefficient is large. In response to these deficiencies, this paper establishes a new improved threshold function based on traditional soft and hard threshold functions. By processing the thresholds of wavelet coefficients, a reasonable balance between smoothing and edge oscillations can be achieved after image denoising. The improved threshold function not only overcomes the shortcomings of the soft and hard threshold functions, but also provides more flexibility in the processing of image noise. Through MATLAB simulation, the denoising effects of the soft, hard threshold functions and the threshold function constructed in this paper are compared in terms of signal-to-noise ratio (SNR) and root mean square error (MSE). The MATLAB simulation results show that compared with the traditional threshold function, the improved threshold function has a higher signal-to-noise ratio (SNR = 26.27709) and a smaller mean square error (MSE = 153.4579), and it has a good noise reduction effect.