A Neurodynamic Optimization Approach for <i>L</i><sub>1</sub> Minimization with Application to Compressed Image Reconstruction
Chengchen Dai, Hangjun Che, Man-Fai Leung
Abstract
This paper presents a neurodynamic optimization approach for l 1 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.
Topics & Concepts
Subgradient methodComputer scienceMinificationAugmented Lagrangian methodImage (mathematics)Mathematical optimizationPoint (geometry)Function (biology)Artificial intelligenceCompressed sensingAlgorithmMachine learningMathematicsProgramming languageGeometryBiologyEvolutionary biologySparse and Compressive Sensing TechniquesNeural Networks and ApplicationsImage and Signal Denoising Methods