[D][R] Is there a general mathematical relationship between denoising autoencoder and a low-pass frequency filter?

Correct me if I am wrong, my understanding is that when a denoising autoencoder is trained to remove noise from input data, it, in a sense, acts as a filter that removes high-frequency noise from the input data, similar to a low-pass filter. By reconstructing clean data from noisy input data, the denoising autoencoder effectively smooths the signal and reduces noise, similar to the behavior of a low-pass filter. My question is, can I express the trained neural network parameters …

autoencoder data denoising filter general low machinelearning noise relationship sense understanding