High Dynamic Range from Multiple Images: Which Exposures to Combine?∗
Michael Grossberg, Shree K. Nayar · 2003
Many computer vision algorithms rely on precise estimates of scene radiances obtained from an image. A simple way to acquire a larger dynamic range of scene radiances is by combining several exposures of the scene. The number of exposures and their values have a dramatic impact on the quality of the combined image. At this point, there exists no principled method to determine these values. Given a camera with known response function and dynamic range, we wish to find the exposures that would result in a set of images that when combined would emulate an effective camera with a desired dynamic range and a desired response function. We first prove that simple summation combines all the information in the individual exposures without loss. We select the exposures by minimizing an objective function that is based on the derivative of the response function. Using our algorithm, we demonstrate the emulation of cameras with a variety of response functions, ranging from linear to logarithmic. We verify our method on several real scenes. Our method makes it possible to construct a table of optimal exposure values. This table can be easily incorporated into a digital camera so that a photographer can emulate a wide variety of high dynamic range cameras by selecting from a menu. 1 Capturing a Flexible Dynamic Range Many computer vision algorithms require accurate estimates of scene radiance such as color constancy [9], inverse rendering [13, 1] and shape recovery [17, 8, 18]. It is difficult to capture both the wide range of radiance values real scenes produce and the subtle variations within them using a low cost digital camera. This is because any camera must assign a limited number of brightness values to the entire range of scene radi-