Operational Support Estimator Networks
Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz, Moncef Gabbouj

TL;DR
This paper introduces Operational Support Estimator Networks (OSENs), a novel non-iterative method for support estimation in sparse signals that improves accuracy and efficiency over traditional iterative techniques.
Contribution
The paper presents OSENs, a new approach with operational layers and generative super neurons that learn complex non-linearities without deep networks, enhancing support estimation performance.
Findings
OSENs outperform traditional methods at low measurement rates.
The approach achieves higher accuracy with computational efficiency.
Experimental results validate the effectiveness across multiple applications.
Abstract
In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the locations of non-zero elements in sparse signals. By its very nature, the mapping between the measurement and sparse signal is a non-linear operation. Traditional support estimators rely on computationally expensive iterative signal recovery techniques to achieve such non-linearity. Contrary to the convolutional layers, the proposed OSEN approach consists of operational layers that can learn such complex non-linearities without the need for deep networks. In this way, the performance of non-iterative support estimation is greatly improved. Moreover, the operational layers comprise so-called generative super neurons with non-local kernels. The kernel location for each neuron/feature map is optimized jointly for…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Photoacoustic and Ultrasonic Imaging · Ultrasonics and Acoustic Wave Propagation
MethodsConvolution
