Priors for Large Photo Collections and What They Reveal about Cameras

A large photo collection downloaded from the internet spans a wide range of scenes, cameras, and photographers. In this paper we introduce several novel priors for statistics of such large photo collections that are independent of these factors. We then propose that properties of these factors can be recovered by examining the deviation between these statistical priors and the statistics of a slice of the overall photo collection that holds one factor constant. Specifically, we recover the radiometric properties of a particular camera model by collecting numerous images captured by it, and examining the deviation of this collection’s statistics from that of a broader photo collection whose camera-specific effects have been removed. We show that using this approach we can recover both a camera model’s non-linear response function and the spatially-varying vignetting of the camera’s different lens settings. All this is achieved using publicly available photographs, without requiring images captured under controlled conditions or physical access to the cameras. We also apply this concept to identify bad pixels on the detectors of specific camera instances. We conclude with a discussion of future applications of this general approach to other common computer vision problems.