Processing of noisy digital images with use of evolving autoencoders

Oleksandr Bezsonov,Oleg Rudenko,Serhii Udovenko,Olga Dudinova

doi:10.15587/1729-4061.2017.116134

Abstract

A promising class of neural network models used recently to solve the problems of recognition of noisy images are denoising autoencoders. In particular, the evolutionary approach can be effectively used in DAE to determine the network architecture, weights and learning algorithm. The proposed neural network evolving autoencoder allows efficient processing of noisy images due to the iterative learning procedure even in the presence of local distortions. When using the EDAE for determining the network architecture, weights and learning algorithm, standard evolutionary procedures (population initialization, population assessment, selection, crossover, mutation), as well as the evolutionary algorithm for the ANN adjustment and special chromosome formats are used. The proposed approach to filtering and recognition of noisy images based on the EDAE application is promising for environmental monitoring of landscape and industrial areas

Highlights

Considerable attention has been paid to the problem of computer analysis of digital images in information systems of various functional purposes, in particular, in geographic information systems (GIS)
A promising class of neural network models applied recently to solve the problems of recognition of noisy images are autoassociative memory models or autoencoders (AE) that have certain advantages over support vector machine (SVM), multilayer perceptron (MLP) and convolutional neural networks (CNN)
The structure of the evolving denoising autoencoder (EDAE) designed to suppress noise and restore distorted fragments in digital images is proposed. This neural network structure is based on the use of intermediate data compression and the evolutionary approach to setting the network parameters and determining the structure

Summary

Introduction

Considerable attention has been paid to the problem of computer analysis of digital images in information systems of various functional purposes, in particular, in geographic information systems (GIS). Analysis and interpretation of aerial photographs (or aerospace images) are an important part of the implementation of many GIS applications These include, in particular, topographic mapping, cadastral mapping, localization of contaminated areas, monitoring of changes in the edges of parts of images. The problem of spatial data processing in GIS for environmental monitoring can be either image improvement (restoration) by some criterion, or a special conversion that purposefully changes the image. Denoising in the problems of spatial data processing in GIS serves to enhance visual perception of the analyzed images This raises the need to improve sharpness during the edge detection, preprocessing and subsequent recognition of image fragments, etc. A promising class of neural network models applied recently to solve the problems of recognition of noisy images are autoassociative memory models or autoencoders (AE) that have certain advantages over SVM, MLP and CNN. The development of a neural network method for image filtering and recognition in the presence of noise and distortions using the modified AE model is relevant

Literature review and problem statement

The aim and objectives of the study

Architecture and learning algorithm of the evolving denoising autoencoder

Discussion of the results of the experimental research of the proposed method

Conclusions