An adaptive image enhancement method for a recirculating aquaculture system

Chao Zhou,Chuanheng Sun,Daming Xu,Qiang Guo,Baihai Zhang,Kai Lin,Xinting Yang

doi:10.1038/s41598-017-06538-9

Abstract

Due to the low and uneven illumination that is typical of a recirculating aquaculture system (RAS), visible and near infrared (NIR) images collected from RASs always have low brightness and contrast. To resolve this issue, this paper proposes an image enhancement method based on the Multi-Scale Retinex (MSR) algorithm and a greyscale nonlinear transformation. First, the images are processed using the MSR algorithm to eliminate the influence of low and uneven illumination. Then, the normalized incomplete Beta function is used to perform a greyscale nonlinear transformation. The function’s optimal parameters (α and β) are automatically selected by the particle swarm optimization (PSO) algorithm based on an image contrast measurement function. This adaptive image enhancement method is compared with other classic enhancement methods. The results show that the proposed method greatly improves the image contrast and highlights dark areas, which is helpful during further analysis of these images.

Highlights

A recirculating aquaculture system (RAS) is a highly efficient artificially controlled system that provides a suitable growth environment for fish through a variety of technologies[1, 2]
The image enhancement was performed by uneven illumination corrections and nonlinear transforms based on the Multi-Scale Retinex (MSR) algorithm and greyscale nonlinear transformation
The raw output data stream from the camera was converted to BMP files by software we developed through the software packages provided by AVT

Summary

Introduction

A recirculating aquaculture system (RAS) is a highly efficient artificially controlled system that provides a suitable growth environment for fish through a variety of technologies[1, 2]. When using the above algorithms to process the resulting images, the parameters for these algorithms cannot be adjusted automatically to match changes in field conditions nor can they completely solve the problem of low contrast caused by a lack of light, unevenness and fish behaviour. To meet real-time processing requirements, an adaptive image enhancement method is needed that can adjust the parameters for intelligent algorithms automatically according to changes in the environment and the monitored objects. On the basis of simulating the commercial-scale fish farm environment, the current study proposes a near infrared and visible image enhancement method to improve the image contrast in the RAS. The purpose of this study was to build a potential method to enhance the contrast of the image in RAS, and we aim to provide accurate and consistent segmentation for subsequent image processing

Objectives

Methods

Results

Conclusion