This paper presents a novel zero-shot method for jointly denoising and enhancing real-word low-light images. The proposed method is independent of training data and noise distribution. Guided by illumination, we integrate denoising and enhancing processes seamlessly, enabling end-to-end training. Pairs of downsampled images are extracted from a single original low-light image and processed to preliminarily reduce noise. Based on the smoothness of illumination, near-authentic illumination can be estimated from the denoised low-light image. Specifically, the illumi-nation is constrained by the denoised image's brightness, uniformly amplifying pixels to raise overall brightness to normal-light level. We simultaneously restrict the illumi-nation by scaling each pixel of the denoised image based on its intensity, controlling the enhancement amplitude for different pixels. Applying the illumination to the original low-light image yields an adaptively enhanced reflection. This prevents under-enhancement and localized overexpo-sure. Notably, we concatenate the reflection with the illumi-nation, preserving their computational relationship, to ul-timately remove noise from the original low-light image in the form of reflection. This provides sufficient image infor-mation for the denoising procedure without changing the noise characteristics. Extensive experiments demonstrate that our method outperforms other state-of-the-art meth-ods. The source code is available at https://github.com/Doyle59217/ZeroIG.