Existing imaging systems support wide-gamut images like ProPhoto RGB, but most images are typically encoded in a narrower gamut space (e.g., sRGB). To this end, these images can be enhanced by learning to recover the original color values beyond the sRGB gamut, or out-of-gamut values. Current methods incorporate the metadata from the target wide-gamut images to expand the gamut, while preventing distortion of in-gamut values. However, this metadata is hard to obtain in real-world scenarios. In this paper, we propose a novel method that requires no metadata. We formulate gamut expansion as a “root-finding” problem and learn an equilibrium transformation via a neural network. Specifically, our method defines a dynamic system that keeps in-gamut values stable to prevent color distortion and updates out-of-gamut values recurrently. Therefore, we employ an implicit recurrent mechanism to iteratively extract features, which can effectively mitigate the vanishing gradient problem, and reduce the GPU memory consumption to \(\mathcal {O}(1)\) complexity during training. Experiments demonstrate the effectiveness and efficiency of our model, in terms of gamut expansion and color restoration, outperforming state-of-the-art models by 0.40 dB, in terms of PSNR, with a size of 40K parameters only. The codes are available at: https://github.com/junxiao01/LETNet .