Wavelet Threshold Denoising Method for Images

The wavelet threshold denoising method stands out among various image denoising techniques. It works by decomposing an image using wavelet transforms, analyzing the distinct characteristics of different sub-band images, and applying appropriate thresholds to achieve optimal noise reduction. This method is applicable to various image types, including natural images, medical images, and engineering images. In implementation, the process typically involves: performing wavelet decomposition using functions like wavedec2() in MATLAB, which separates the image into approximation and detail coefficients; analyzing the statistical properties of detail coefficients in different sub-bands; selecting appropriate threshold values using methods like VisuShrink or SureShrink; and applying thresholding functions (hard or soft thresholding) to suppress noise while preserving image details. By applying wavelet decomposition with proper threshold selection, this method effectively reduces image noise while enhancing image quality. In practical applications, wavelet threshold denoising is commonly used to process blurred or noisy images to obtain clearer and more accurate results. Therefore, understanding and mastering this method is crucial for research and applications in the image processing field.