  Traditional supervised denoising networks learn network weights through
"black box" (pixel-oriented) training, which requires clean training labels.
The uninterpretability nature of such denoising networks in addition to the
requirement for clean data as labels limits their applicability in real case
scenarios. Deep unfolding methods unroll an optimization process into Deep
Neural Networks (DNNs), improving the interpretability of networks. Also,
modifiable filters in DNNs allow us to embed the physics information of the
desired signals to be extracted, in order to remove noise in a self-supervised
manner. Thus, we propose a Gabor-based learnable sparse representation network
to suppress different noise types in a self-supervised fashion through
constraints/bounds applied to the parameters of the Gabor filters of the
network during the training stage. The effectiveness of the proposed method was
demonstrated on two noise type examples, pseudo-random noise and ground roll,
on synthetic and real data.
