Data and model dual-driven seismic deconvolution via error-constrained joint sparse representation
Y Wang, G Zhang, T Chen, Y Liu, B Shen… - …, 2023 - pubs.geoscienceworld.org
Deconvolution is an essential step in seismic data processing. Sparse-spike deconvolution
often is used to enhance the resolution of the seismic image by adding a model-driven …
often is used to enhance the resolution of the seismic image by adding a model-driven …
Seismic multichannel deconvolution via 2-D K-SVD and MSD-oCSC
Y Wang, X Gao, G Zhang, B Zou… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The deconvolution method is crucial for enhancing seismic resolution. Traditional
multichannel schemes incorporate lateral constraints to enhance data continuity and …
multichannel schemes incorporate lateral constraints to enhance data continuity and …
A two-stage convolutional sparse coding network for hyperspectral image classification
C Cheng, J Peng, W Cui - IEEE Geoscience and Remote …, 2023 - ieeexplore.ieee.org
The convolutional sparse coding (CSC) can learn shift-invariant convolution kernels. In deep
convolutional neural networks, it takes a lot of time to train the convolution kernels. In this …
convolutional neural networks, it takes a lot of time to train the convolution kernels. In this …
Less is more: Rethinking few-shot learning and recurrent neural nets
The statistical supervised learning framework assumes an input-output set with a joint
probability distribution that is reliably represented by the training dataset. The learner is then …
probability distribution that is reliably represented by the training dataset. The learner is then …
One-Shot Image Restoration
D Pereg - arxiv preprint arxiv:2404.17426, 2024 - arxiv.org
Image restoration, or inverse problems in image processing, has long been an extensively
studied topic. In recent years supervised learning approaches have become a popular …
studied topic. In recent years supervised learning approaches have become a popular …
Back to basics: Fast denoising iterative algorithm
D Pereg - Signal Processing, 2024 - Elsevier
Abstract We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction. Our
method is computationally efficient, does not require training or ground truth data, and can …
method is computationally efficient, does not require training or ground truth data, and can …
Back to Basics: Fast Denoising Iterative Algorithm
D Pereg - arxiv preprint arxiv:2311.06634, 2023 - arxiv.org
We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction. Our method
is computationally efficient, does not require training or ground truth data, and can be …
is computationally efficient, does not require training or ground truth data, and can be …