Image denoising is an important and difficult problem in image processing. In classic blind denoising solutions, dependency on assumed noise priors usually leads to an inherent drawback. The nature of the noise these priors are supposed to represent can be misleading, leaving less-than-optimal performance. To mitigate this poor constraint, here we present a novel framework: the Enhanced Wavelet Transform with Adaptive Filter (EWT-AF).EWT-AF leverages a multi-channel Discrete Wavelet Transform (DWT) network which is paired with a model that correctly identifies and adjusts to different types of noise using prior knowledge distilling. Furthermore, the Adaptive Dynamic Regulation Filter (ADRF) autonomously adjusts parameters, ensuring the optimal configuration for each scenario. More on the process the Pretrained Dynamic Mask Model (PDMM) is trained on natural image data to fine-tune the image details. Comprehensive experimental results illustrate that EWT-AF achieves better denoising performance on various conditions, being competitive on both known and blind denoising tasks.