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Found 17 papers, 2 papers with code
Physics-Inspired Synthesized Underwater Image Dataset
This paper introduces the physics-inspired synthesized underwater image dataset (PHISWID), a dataset tailored for enhancing underwater image processing through physics-inspired image synthesis.
Lossy Compression of Adjacency Matrices by Graph Filter Banks
In this paper, we propose a lossy compression of weighted adjacency matrices, where the binary adjacency information is encoded losslessly (so the topological information of the graph is preserved) while the edge weights are compressed lossily.
Optimizing $k$ in $k$NN Graphs with Graph Learning Perspective
$k$NN is one of the most popular approaches and is widely used in machine learning and signal processing.
Clustering of Time-Varying Graphs Based on Temporal Label Smoothness
In this paper, we formulate a node clustering of time-varying graphs as an optimization problem based on spectral clustering, with a smoothness constraint of the node labels.
Attention-based Graph Convolution Fusing Latent Structures and Multiple Features for Graph Neural Networks
Instead, we propose two methods to improve the representational power of AGCs by utilizing 1) structural information in a high-dimensional space and 2) multiple attention functions when calculating their weights.
Dynamic Sensor Placement Based on Graph Sampling Theory
In this paper, we consider a dynamic sensor placement problem where sensors can move within a network over time.
Multi-channel Sampling on Graphs and Its Relationship to Graph Filter Banks
In this paper, we consider multi-channel sampling (MCS) for graph signals.
Regularity-Constrained Fast Sine Transforms
In contrast, the proposed regularity-constrained fast sine transform (R-FST) is obtained by just appending a regularity constraint matrix as a postprocessing of the original DST.
Graph Signal Sampling Under Stochastic Priors
We propose a generalized sampling framework for stochastic graph signals.
Directional Analytic Discrete Cosine Frames
Block frames called directional analytic discrete cosine frames (DADCFs) are proposed for sparse image representation.
Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networks
In this paper, we propose two methods to improve the performance of GCs: 1) Utilizing structural information in the feature space, and 2) exploiting the multi-hop information in one GC step.
Graph Signal Restoration Using Nested Deep Algorithm Unrolling
We overcome two main challenges in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually.
Marine Snow Removal Benchmarking Dataset
This paper introduces a new benchmarking dataset for marine snow removal of underwater images.
Graph Blind Deconvolution with Sparseness Constraint
We propose a blind deconvolution method for signals on graphs, with the exact sparseness constraint for the original signal.
Sampling Signals on Graphs: From Theory to Applications
In this article, we review current progress on sampling over graphs focusing on theory and potential applications.
Time-Varying Graph Learning with Constraints on Graph Temporal Variation
We propose a novel framework for learning time-varying graphs from spatiotemporal measurements.
Fast Singular Value Shrinkage with Chebyshev Polynomial Approximation Based on Signal Sparsity
We propose an approximation method for thresholding of singular values using Chebyshev polynomial approximation (CPA).
