Operational Support Estimator Networks

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 the SE task during training. We evaluate the OSENs in three different applications: i. support estimation from Compressive Sensing (CS) measurements, ii. representation-based classification, and iii. learning-aided CS reconstruction where the output of OSENs is used as prior knowledge to the CS algorithm for enhanced reconstruction. Experimental results show that the proposed approach achieves computational efficiency and outperforms competing methods, especially at low measurement rates by significant margins. The software implementation is shared at https://github.com/meteahishali/OSEN.