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Found 23 papers, 3 papers with code
Neuromorphic Sampling of Sparse Signals
We develop a kernel-based sampling approach, which allows for perfect reconstruction with a sample complexity equal to the rate of innovation of the signal.
Tight-frame-like Analysis-Sparse Recovery Using Non-tight Sensing Matrices
We consider the analysis-sparse l1-minimization problem with a generalized l2-norm-based data-fidelity and show that it effectively corresponds to using a tight-frame sensing matrix.
Neuromorphic Sampling of Signals in Shift-Invariant Spaces
We present an iterative technique for perfect reconstruction subject to the events satisfying a density criterion.
GANs Settle Scores!
Generative adversarial networks (GANs) comprise a generator, trained to learn the underlying distribution of the desired data, and a discriminator, trained to distinguish real samples from those output by the generator.
Data Interpolants -- That's What Discriminators in Higher-order Gradient-regularized GANs Are
We show analytically, via the least-squares (LSGAN) and Wasserstein (WGAN) GAN variants, that the discriminator optimization problem is one of interpolation in $n$-dimensions.
Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training
We demonstrate the efficacy of the Spider approach on DCGAN, conditional GAN, PGGAN, StyleGAN2 and StyleGAN3.
Wavelet Design in a Learning Framework
Wavelets have proven to be highly successful in several signal and image processing applications.
Time Encoding of Finite-Rate-of-Innovation Signals
Time-encoding of continuous-time signals is an alternative sampling paradigm to conventional methods such as Shannon's sampling.
Learning Generative Prior with Latent Space Sparsity Constraints
We also consider the effect of the dimension of the latent space and the sparsity factor in validating the SDLSS framework.
Quantized Proximal Averaging Network for Analysis Sparse Coding
We also consider quantization of the network weights.
NuSPAN: A Proximal Average Network for Nonuniform Sparse Model -- Application to Seismic Reflectivity Inversion
We solve the problem of sparse signal deconvolution in the context of seismic reflectivity inversion, which pertains to high-resolution recovery of the subsurface reflection coefficients.
DuRIN: A Deep-unfolded Sparse Seismic Reflectivity Inversion Network
The network is referred to as deep-unfolded reflectivity inversion network (DuRIN).
Robust Segmentation of Optic Disc and Cup from Fundus Images Using Deep Neural Networks
Further, we perform two-stage glaucoma severity grading using the cup-to-disc ratio (CDR) computed based on the obtained OD/OC segmentation.
Teaching a GAN What Not to Learn
Generative adversarial networks (GANs) were originally envisioned as unsupervised generative models that learn to follow a target distribution.
Quantization-Aware Phase Retrieval
A comparison with the state-of-the- art algorithms shows that the proposed algorithm has a higher reconstruction accuracy and is about 2 to 3 dB away from the CRB.
Epoch-Synchronous Overlap-Add (ESOLA) for Time- and Pitch-Scale Modification of Speech Signals
Time- and pitch-scale modifications of speech signals find important applications in speech synthesis, playback systems, voice conversion, learning/hearing aids, etc..
Online Reweighted Least Squares Algorithm for Sparse Recovery and Application to Short-Wave Infrared Imaging
The proposed online algorithm is useful in a setting where one seeks to design a progressive decoding strategy to reconstruct a sparse signal from linear measurements so that one does not have to wait until all measurements are acquired.
Deep Sparse Coding Using Optimized Linear Expansion of Thresholds
We address the problem of reconstructing sparse signals from noisy and compressive measurements using a feed-forward deep neural network (DNN) with an architecture motivated by the iterative shrinkage-thresholding algorithm (ISTA).
Risk Estimation Without Using Stein's Lemma -- Application to Image Denoising
For the case of additive white Gaussian noise contamination, the risk estimation procedure relies on Stein's lemma.
Directional Bilateral Filters
We propose a bilateral filter with a locally controlled domain kernel for directional edge-preserving smoothing.
$\ell_1$-K-SVD: A Robust Dictionary Learning Algorithm With Simultaneous Update
We develop a dictionary learning algorithm by minimizing the $\ell_1$ distortion metric on the data term, which is known to be robust for non-Gaussian noise contamination.
A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation
We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy operating on the entire data at a time.
Template-Based Active Contours
We show that the contrast energy functional is optimal under certain conditions.
