A Neurodynamic Optimization Approach for L1 Minimization with Application to Compressed Image Reconstruction

Chengchen Dai; Hangjun Che; Man Fai Leung

doi:10.1142/S0218213021400078

A Neurodynamic Optimization Approach for L1 Minimization with Application to Compressed Image Reconstruction

Chengchen Dai
, Hangjun Che
, Man Fai Leung

Hong Kong Metropolitan University

Research output: Contribution to journal › Article › peer-review

29 Citations (Scopus)

Abstract

This paper presents a neurodynamic optimization approach for l1 minimization based on an augmented Lagrangian function. By using the threshold function in locally competitive algorithm (LCA), subgradient at a nondifferential point is equivalently replaced with the difference of the neuronal state and its mapping. The efficacy of the proposed approach is substantiated by reconstructing three compressed images.

Original language	English
Article number	2140007
Journal	International Journal on Artificial Intelligence Tools
Volume	30
Issue number	1
DOIs	https://doi.org/10.1142/S0218213021400078
Publication status	Published - Feb 2021

Keywords

compressed image reconstruction
l1 minimization
Neurodynamic optimization

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10.1142/S0218213021400078

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abstract = "This paper presents a neurodynamic optimization approach for l1 minimization based on an augmented Lagrangian function. By using the threshold function in locally competitive algorithm (LCA), subgradient at a nondifferential point is equivalently replaced with the difference of the neuronal state and its mapping. The efficacy of the proposed approach is substantiated by reconstructing three compressed images.",

keywords = "compressed image reconstruction, l1 minimization, Neurodynamic optimization",

author = "Chengchen Dai and Hangjun Che and Leung, \{Man Fai\}",

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KW - compressed image reconstruction

KW - l1 minimization

KW - Neurodynamic optimization

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A Neurodynamic Optimization Approach for L1 Minimization with Application to Compressed Image Reconstruction

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Keywords

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