A Simple Spatial Tone Mapping Operator for High Dynamic Range Images

We present a simple and effective tone mapping operator, that preserves visibility and contrast impression of high dynamic range images. The method is conceptually simple, and easy to use. We use a s-function type operator which takes into account both the global average of the image, as well as local luminance in the immediate neighborhood of each pixel. The local luminance is computed using a median filter. It is seen that the resulting low dynamic range image preserves fine details, and avoids common artifacts such as halos, gradient reversals or loss of local contrast. Introduction The real world scenes often have a very high range of luminance values. While digital imaging technology now enables us to capture full dynamic range of the real world scene, still we are limited by the low dynamic range displays. Thus the scene can be visualized on a display monitor only after the captured high dynamic range is compressed to available range of the display device. This has been referred to as the tone mapping problem in the literature and a great deal of work has been done in this area by using a mapping that varies spatially depending on the neighborhood of a pixel, often at multiple scales [Pattanaik et.al. 1998; Fattal et.al. 2002;Reinhard et. al 2002; Durand and Dorsey 2002]. [Johnson and Fairchild 2003] have shown how accurate color predictions can be made for tone mapping high dynamic range images. The rendering performance of some of these algorithms has been recently reported in [Kuang et. al 2004]. In this paper we propose a simple tone mapping operator which allows us to preserve the visual content of the real-world scene without the user having to manually set a number of parameters. We show that by using a log of relative luminance at a pixel with respect to its local luminance in a small neighborhood, the standard s-function can be modified to yield visually pleasing results. Further it is also proposed that the local luminance be computed using a median filter, which provides a stronger central indicator than the mean filter. The operator The global contrast helps us to differentiate between various regions of the HDR (high dynamic range) image, which we can loosely classify as dark, dim, lighted, bright etc. Within each region objects become distinguishable due to local contrast against the background – either the object is darker than the background or it is brighter than the background. If the HDR image consisted of only regions of uniform illuminations, the following s-function would compress the range of illumination across the image, to displayable luminance YD in the range 0 -1. YD(x, y) = Y(x, y) / [Y(x, y) + GC] (1) where GC is the global contrast factor computed through