| Summary: | Hard thresholding is a common method for denoising wavelet coefficients. However, determining an optimal threshold remains challenging. Therefore, we propose an adaptive hard threshold that accounts for the size of the noisy image based on the statistical properties of the wavelet coefficient. Regression analysis is used to determine the adaptive threshold to ensure effective performance with multiresolution wavelet coefficients. The adaptive threshold enables a balance between denoising and high-frequency detail preservation, and excellent denoising performance. Multiple segmentations are conducted using the multiresolution properties of the wavelet transform, based on both image size and noise level, leading to the best denoising results.
|
|---|
