A comparative analysis of illumination estimation based Image Enhancement techniques

Computer vision has gathered the attention of several researchers due to the incremental use of it in almost every field. The performance of computer vision systems is many times limited by input image quality. Thus, image enhancement becomes an essential step in these systems. Retinex based algorithms have proven performance in enhancement of low light images. Many Retinex-based algorithms focus on illumination estimation to perform image enhancement. This paper presents a comparative analysis of state-of-the-art image enhancement algorithms based on illumination estimation. In Retinex based approaches, computation of illumination and reflectance is a challenging task. In early approaches, a smooth image is considered as the illumination. However, in the last decade, various methods for estimation of illumination and reflectance have evolved up to a great extent. In this work, we analyze these image enhancement techniques based on illumination estimation. We perform extensive experimentation on a large set of images with varying illumination. The performance is analyzed both quantitatively and qualitatively. This analysis aims to assist researchers and ignite research to develop new efficient algorithms in this field.