Abstract

Retinex model is an effective tool for low-light image enhancement. It assumes that observed images can be decomposed into the reflectance and illumination. Most existing Retinex-based methods have carefully designed hand-crafted constraints and parameters for this highly ill-posed decomposition, which may be limited by model capacity when applied in various scenes. In this paper, we collect a LOw-Light dataset (LOL) containing low/normal-light image pairs and propose a deep Retinex-Net learned on this dataset, including a Decom-Net for decomposition and an Enhance-Net for illumination adjustment. In the training process for Decom-Net, there is no ground truth of decomposed reflectance and illumination. The network is learned with only key constraints including the consistent reflectance shared by paired low/normal-light images, and the smoothness of illumination. Based on the decomposition, subsequent lightness enhancement is conducted on illumination by an enhancement network called Enhance-Net, and for joint denoising there is a denoising operation on reflectance. The Retinex-Net is end-to-end trainable, so that the learned decomposition is by nature good for lightness adjustment. Extensive experiments demonstrate that our method not only achieves visually pleas- ing quality for low-light enhancement but also provides a good representation of image decomposition.

Subjective Results 

We compare our Retinex-Net with four state-of-the-art methods, including de-hazing based method (DeHz) [1], naturalness preserved enhancement algorithm (NPE) [2], simultaneous reflectance and illumination estimation algorithm (SRIE) [3], and illumination map estimation based (LIME) [4]. Fig. 2 shows visual comparison on three natural images.

Figure 2: The results using different methods on natural images: (top-to-bottom) Street from LIME dataset [4], Still lives from LIME dataset, and Room from MEF dataset [5].

We compare our joint-denoising Retinex-Net with two methods, one is LIME with denoising post-processing, the other is JED [6], a recent joint low-light enhancement and denoising method. Fig. 3 shows visual comparison on three natural images.

Figure 4: The decomposition results using our Decom-Net and LIME on Bookshelf in LOL dataset. In our results, the reflectance of the low-light image resembles the reflectance of the normal-light image except for the amplified noise in dark regions occurred in real scenes.