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Found 5 papers, 0 papers with code
Back to Basics: Fast Denoising Iterative Algorithm
We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction.
Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction
In recent years, there have been significant advances in leveraging deep learning methods for noise reduction.
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.
Convolutional Sparse Coding Fast Approximation with Application to Seismic Reflectivity Estimation
In sparse coding, we attempt to extract features of input vectors, assuming that the data is inherently structured as a sparse superposition of basic building blocks.
Blind Deconvolution via Maximum Kurtosis Adaptive Filtering
In this paper a successful attempt has been made to apply the algorithm to a wider range of signals, such as to process distorted audio signals and destructed images.
