Image Guided Tone Mapping with Locally Nonlinear Model

Huxiang Gu, Ying Wang, Shiming Xiang, Gaofeng Meng, and Chunhong Pan

Abstract. In this paper, we propose an effective locally nonlinear tone mapping algorithm for compressing the High Dynamic Range (HDR) images. Instead of linearly scaling the luminance of pixels, our core idea is to introduce local gamma correction with adaptive parameters on small overlapping patches over the entire input image. A framework for HDR image compression is then introduced, in which the global optimization problem is deduced and two guided images are adopted to induct the optimum solution. The optimal compression can finally be achieved by solving the optimization problem which can be transformed to a sparse linear equation. Extensive experimental results on a variety of HDR images and a carefully designed perceptually evaluation have demonstrated that our approach can achieve better performances than the state-of-the-art approaches.

Keywords: high dynamic range, tone mapping, locally nonlinear model, guided image

LNCS 7575, p. 786 ff.

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