Signal processing in light-microscopy and cell imaging is concerned with reconstructing latent ground truth from imperfect images. This typically requires assuming prior knowledge about the latent ground truth. While this assumption regularizes the problem to an extent where it can be solved, it also biases the result toward the expected. It thus often remains unclear what prior to use for a given practical problem. We argue here that the gradient distribution of natural-scene images may provide a versatile and well-founded prior for light-microscopy images that does not impose assumptions about the geometry of the ground-truth signal, but only about its gradient spectrum. We provide motivation for this choice from different points of view, and we illustrate the resulting regularizer for use on light-microscopy images. We provide a simple parametric model for the resulting prior, leading to efficiently solvable variational problems. We demonstrate the use of these models and solvers in a variety of common image-processing tasks, including contrast enhancement, noise-level estimation, denoising, blind deconvolution, and dehazing. We conclude by discussing the limitations and possible interpretations of the prior.