Gabor-based learnable sparse representation for self-supervised denoising

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number5903016
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume62
DOIs
StateAccepted/In press - 2024

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • deep learning
  • Dictionaries
  • Gabor filters
  • Noise measurement
  • Noise reduction
  • seismic denoising
  • Self-supervised
  • Task analysis
  • Training
  • Transforms

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences

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