In this study, we address a fundamental and still relatively less explored aspect in the field of neural networks for image dehazing: the unsupervised dehazing of an image. By conceiving a hazy image as the superposition of several “simpler“ layers, such as a haze-free image layer, a transmission map layer, and an atmospheric light layer, inspired by the atmospheric scattering model, we propose an approach based on the concept of layer disentangling. Our method, called XYZ, represents a substantial improvement in image quality metrics, such as SSIM and PSNR as well as BRISQUE, PIQE and NIQE. This advancement is achieved through the strategic combination of the XHOT, YOLY and ZID methods, capitalizing on the individual strengths of each. A distinctive and valuable aspect of the XYZ approach is its unsupervised nature, which implies that it does not rely on data sets containing pairs o...