Anyone who has applied digital photography tools on a computer has utilized image denoising algorithms. The magical contrast and brightness adjustments that many of them contain are examples that often lead to significant improvements in the appearance of the photo. (One of your SIAM Review editors recalls a poorly exposed photo from an Oxford street on a gray day, where these tools made a dazzling array of building colors emerge from what had been a drab, monochromatic image.) The SIGEST paper in this issue offers a broad and enlightening view into the state of the art for research and approaches in image denoising. The paper “Image Denoising Methods. A New Nonlocal Principle” by A. Baudes, B. Coll, and J. M. Morel originally was published in 2005 in Multiscale Modeling and Simulation under the title “A Review of Image Denoising Algorithms, with a New One.” The paper examines the performance of a variety of classical image denoising algorithms, including Gaussian smoothing, anisotropic filtering, a variant of wavelet thresholding, and several others, utilizing a newly defined mathematical and experimental methodology for the comparison. The comparison methodology is based upon the analysis of “method noise,” the difference between a digital image and its denoised version. The paper also proposes a new, “nonlocal means” algorithm for image denoising, which has become well known and important, and includes this algorithm in the comparisons. For the SIGEST version, the authors have significantly revised their already excellent paper in ways that make it even more useful to the general community of SIAM readers. Most significantly, they have added an extensive final section that summarizes the developments in nonlocal means algorithms in the last five years. The nonlocal means approach has proven very useful and influential, and has been extended to a number of additional image processing tasks and improved in various ways. The authors also have removed some proofs and appendices from the original paper, and have incorporated a new set of images to illustrate algorithmic performance. These revisions, coupled with the survey aspects of the paper, make it essential reading for anyone interested in the state of the art in image denoising. And now you can think of SIAM when you edit your digital photos!