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 illumination is constrained by the denoised image's brightness, uniformly amplifying pixels to raise overall brightness to normal-light level. We simultaneously restrict the illumination 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 overexposure. Notably, we concatenate the reflection with the illumination, preserving their computational relationship, to ultimately remove noise from the original low-light image in the form of reflection. This provides sufficient image information for the denoising procedure without changing the noise characteristics. Extensive experiments demonstrate that our method outperforms other state-of-the-art methods. The source code is available at https://github.com/Doyle59217/ZeroIG.