.png){kind=icon}
Return
Gabor-Based Learnable Sparse Representation for Self-Supervised Denoising
.png){kind=icon}
OA
AI
DOI：10.1109/TGRS.2024.3353315.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Traditional supervised denoising networks learn network weights through black box (pixel-oriented) training, which requires clean training labels. The inability of such denoising networks to interpret their behavior and the requirement for clean data as labels limit 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 prior 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 is demonstrated on two noise type examples, pseudo-random noise and ground roll, on synthetic and real data.
Keywords:
Deep learning
seismic denoising
self-supervised
Journal
IF:
8.6
Papers:
2.2W
Citations:
10.7W
Organization
Cited Papers
ShareSaveInterpolation and denoising of high-dimensional seismic data by learning a tight frame.png)
GEOPHYSICS
GEOPHYSICSIF3.2
PREAI
ShareSaveA method of combining coherence-constrained sparse coding and dictionary learning for denoisingGEOPHYSICS
OAAI
GEOPHYSICSIF3.2
OAAIShareSaveShareSave
View more