RETINEXUTV: ROBUST RETINEX MODEL WITH UNFOLDING TOTAL VARIATION

Abstract

Digital images are underexposed due to poor scene lighting or hardware limitations, reducing visibility and level of detail in the image, which will affect subsequent high-level tasks and image aesthetics. Therefore, it is of great practical significance to enhance low-light images. Among existing low-light image enhancement techniques, Retinex-based methods are the focus today. However, most Retinex methods either ignore or poorly handle noise during enhancement, which can produce unpleasant visual effects in low-light image enhancement and affect high-level tasks. In this paper, we propose a robust low-light image enhancement method RetinexUTV, which aims to enhance low-light images well while suppressing noise. In RetinexUTV, we propose an adaptive illumination estimation unfolded total variational network, which approximates the noise level of the real low-light image by learning the balance parameter of the total variation regularization term of the model, obtains the noise level map and the smooth noise-free sub-map of the image. The initial illumination map is then estimated by obtaining the illumination information of the smooth sub-map. The initial reflection map is obtained through the initial illumination map and original image. Under the guidance of the noise level map, the noise of the reflection map is suppressed, and finally it is multiplied by the adjusted illumination map to obtain the final enhancement result. We test our method on real low-light datasets LOL, VELOL, and experiments demonstrate that our method outperforms state-of-theart methods.

1. INTRODUCTION

Recording people's lives by taking pictures or videos is becoming more and more popular. However, since most users lack professional shooting skills, many photos are captured in less than ideal lighting conditions such as night time and backlight. Such images have low contrast, strong noise, and unclear details, which not only affect the human visual experience, but also limit the application of many computer vision algorithms, such as object recognition and object detection. There are several approaches to enhance low-light images, including histogram equalization (Abdullah-Al-Wadud et al., 2007; Pizer, 1990) , inverse domain operations (Li et al., 2015; Zhang et al., 2016) , Retinex decomposition (Xiao & Shi, 2013; Jobson et al., 1997a; b; Herscovitz & Yadid-Pecht, 2004 ) and deep learning (Yang et al., 2016; Lore et al., 2017) . Histogram equalization-based methods flatten the histogram and stretch the dynamic range of intensity, thereby amplifying the illumination of low-light images. If noise is not specifically considered, noise and artifacts will be amplified in its results. Some researchers noticed the similarity between haze images and inverted low-light images. Therefore, these inverse domain-based methods apply de-overlapping methods to enhance low-light images. To jointly adjust illumination and suppress noise, a method based on Retinex theory is proposed. Methods based on Retinex decomposition treat the scene in the human eye as the product of the reflection layer and the illumination layer. Enhanced results are produced by adjusting the corresponding layers. The earliest methods directly regard the decomposed reflection layer as the enhancement result (Jobson et al., 1997b; a; Xiao & Shi, 2013; Herscovitz & Yadid-Pecht, 2004) . Single-scale Retinex (SSR) (Jobson et al., 1997b) and Multi-scale Retinex (MSR) (Jobson et al., 1997a) utilize Gaussian filters to build Retinex representations. In (Xiao & Shi, 2013) , a bilateral filter is used to remove halo artifacts. Later methods adjust the illumination and reflection layers and reconstruct the enhanced result by combining them. In (Kimmel et al., 

