Photographic tone reproduction for digital images

Abstract

A classic photographic task is the mapping of the potentially high dynamic range of real world luminances to the low dynamic range of the photographic print. This tone reproduction problem is also faced by computer graphics practitioners who map digital images to a low dynamic range print or screen. The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator. In particular, we use and extend the techniques developed by Ansel Adams to deal with digital images. The resulting algorithm is simple and produces good results for a wide variety of images.

Adaptive quantization using piecewise companding and scaling for Gaussian mixture

“…13(a). Also the halo artifacts brought by multiscale in our algorithm is reduced by post processing with our methods, as Furthermore, we compare our method 1 with different N with histogram based method and Erik Reinhard's method [20] as shown in Fig. 15 and Fig.…”

“…F. Drago et al used an adaptive logrithmic mapping for displaying high contrast scenes [19]. Erik Reinhard et al [20] developed their tone mapping method based on the well-known photo-graphic pratice of dodging-and-burning. Fattal et al [21] manipulate scale factors in the gradient domain of logarithmic space.…”

Dynamic range expansion based on image statistics

“…One of the pioneering works in the field is that of Banterle et al [6,8], who define their expansion map by density estimation of the bright areas in the input image, and use the inverse of Reinhard's tone mapping operator [22] to boost the dynamic range. A further extension of the method allows handling video by ensuring temporal coherence of the expansion map between frames [7].…”

An Effective Tone Mapping Operator for High Dynamic Range Images

“…A number of papers were presented on the SIGGRAPH, Eurographics, and Graphicon conferences [9][10][11][12][13][14][15]. Most of the algorithms presented in these works can successfully be applied to both real images (for example, photographs) with high dynamic range of brightness and to the images created by physically based lighting simulation system.…”

“…For the algorithms of this kind, the most difficult and computationally expensive problem is to correctly determine the size of the local neighborhood for each pixel. If this size is determined incorrectly, various artifacts may arise (example of such artifacts are presented in [12]). …”