Performance Analysis of Retinex Based Algorithms for Enhancement of Low Light Images

Abstract

The images captured under low and poor light conditions may suffer low contrast and blurring problems which will affect its interpretation and recognition. Hence it is important to enhance and restore such low contrast images. The enhancement of the captured images in the low light environment through various image enhancement algorithms is discussed in this work. The set of algorithms tested includes single scale retinex, multiscale retinex color preservation, multiscale retinex color restoration, contrast limited enhanced adaptive histogram equalization, dark channel prior, and gamma correction algorithms. This paper discusses the various variants of retinex models, which are considered powerful methods for image enhancement and restoration. The proposed work involves the study of various image enhancement algorithms and their comparison on the basis of a reference original low light image and human eye perception models. The performance of various retinex derivative algorithms is experimented with using the LIME dataset, and the histogram equalization is analyzed for metrics such as peak signal to noise ratio, structural similarity index, and discrete entropy.