Fast &amp; Robust Image Interpolation Using Gradient Graph Laplacian Regularizer

Chen, Fei; Cheung, Gene; Zhang, Xue

doi:10.1109/icip42928.2021.9506064

Cited by 13 publications

(2 citation statements)

References 28 publications

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“…We obtain the mathematical derivation of "x" and "y" by entering all values of VEV in Eqs. ( 3) and (5).…”

Section: Proposed Vht Feature Extractormentioning

confidence: 99%

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

Rashid,

Raza,

Sharif

et al. 2023

Sci Rep

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White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learning model in combination with our proposed virtual hexagonal trellis (VHT) structure feature extraction method. The VHT feature extractor is a kernel-based filter model designed over a square lattice. In the first step, Graft Net CNN model is used to extract features of augmented data set images. Later, the VHT base feature extractor extracts useful features. The CNN-extracted features are passed to ant colony optimization (ACO) module for optimal features acquisition. Extracted features from the VHT base filter and ACO are serially merged to create a single feature vector. The merged features are passed to the support vector machine (SVM) variants for optimal classification. Our strategy yields 99.9% accuracy, which outperforms other existing methods.

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“…We obtain the mathematical derivation of "x" and "y" by entering all values of VEV in Eqs. ( 3) and (5).…”

Section: Proposed Vht Feature Extractormentioning

confidence: 99%

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

Rashid,

Raza,

Sharif

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…A key assumption in GSP is that an underlying similarity graph capturing pairwise correlations is available as input, before spectral filters are designed and applied to signals on top based on spectral graph theory [13]. Given a training set of graph signal observations generated from the same statistical model, there exist many graph learning algorithms [14]- [16] that compute a most likely sparse inverse covariance matrix (interpreted as a generalized graph Laplacian matrix), which is subsequently used for GSP processing like compression [17], [18], denoising [19], [20] and interpolation [21].…”

Section: Introductionmentioning

confidence: 99%

Sparse Graph Learning with Eigen-gap for Spectral Filter Training in Graph Convolutional Networks

Zeng¹,

Bagheri²,

Liu³

et al. 2022

Preprint

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It is now known that the expressive power of graph convolutional neural nets (GCN) does not grow infinitely with the number of layers. Instead, the GCN output approaches a subspace spanned by the first eigenvector of the normalized graph Laplacian matrix with the convergence rate characterized by the "eigen-gap": the difference between the Laplacian's first two distinct eigenvalues. To promote a deeper GCN architecture with sufficient expressiveness, in this paper, given an empirical covariance matrix C computed from observable data, we learn a sparse graph Laplacian matrix L closest to C−1 while maintaining a desirable eigen-gap that slows down convergence. Specifically, we first define a sparse graph learning problem with constraints on the first eigenvector (the most common signal) and the eigen-gap. We solve the corresponding dual problem greedily, where a locally optimal eigen-pair is computed one at a time via a fast approximation of a semi-definite programming (SDP) formulation. The computed L with the desired eigen-gap is normalized spectrally and used for supervised training of GCN for a targeted task. Experiments show that our proposal produced deeper GCNs and smaller errors compared to a competing scheme without explicit eigen-gap optimization.

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Fast Graph Sampling for Short Video Summarization Using Gershgorin Disc Alignment

Sahami

Cheung

Lin

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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User-generated videos (UGVs) uploaded from mobile phones to social media sites like YouTube and TikTok are short and non-repetitive. We summarize a transitory UGV into several keyframes in linear time via fast graph sampling based on Gershgorin disc alignment (GDA). Specifically, we first model a sequence of N frames in a UGV as an M -hop path graph G o for M ≪ N , where the similarity between two frames within M time instants is encoded as a positive edge based on feature similarity. Towards efficient sampling, we then "unfold" G o to a 1-hop path graph G, specified by a generalized graph Laplacian matrix L, via one of two graph unfolding procedures with provable performance bounds. We show that maximizing the smallest eigenvalue λmin(B) of a coefficient matrix B = diag(h) + µL, where h is the binary keyframe selection vector, is equivalent to minimizing a worst-case signal reconstruction error. We maximize instead the Gershgorin circle theorem (GCT) lower bound λ − min (B) by choosing h via a new fast graph sampling algorithm that iteratively aligns left-ends of Gershgorin discs for all graph nodes (frames). Extensive experiments on multiple short video datasets show that our algorithm achieves comparable or better video summarization performance compared to state-ofthe-art methods, at a substantially reduced complexity.

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Fast & Robust Image Interpolation Using Gradient Graph Laplacian Regularizer

Cited by 13 publications

References 28 publications

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

Sparse Graph Learning with Eigen-gap for Spectral Filter Training in Graph Convolutional Networks

Fast Graph Sampling for Short Video Summarization Using Gershgorin Disc Alignment

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