Title: 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-the-art methods.