Reservoir Computing based Neural Image Filters

TL;DR

This paper explores using reservoir computing, a biologically inspired neural network model, to develop dynamic image filters capable of extracting signals from noisy images, with potential hardware implementation near sensors.

Contribution

It introduces a novel approach applying reservoir computing to dynamic image filtering and discusses hardware implementation possibilities.

Findings

Reservoir computing can effectively filter noisy images.

Potential for hardware implementation close to sensors.

Enhances image quality for machine vision systems.

Abstract

Clean images are an important requirement for machine vision systems to recognize visual features correctly. However, the environment, optics, electronics of the physical imaging systems can introduce extreme distortions and noise in the acquired images. In this work, we explore the use of reservoir computing, a dynamical neural network model inspired from biological systems, in creating dynamic image filtering systems that extracts signal from noise using inverse modeling. We discuss the possibility of implementing these networks in hardware close to the sensors.