Tone Mapping Operators for High Dynamic Range Images
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The conversion of High Dynamic Range (HDR) image into Low Dynamic Range (LDR) image is investigated so that the visual rendering of the latter is of good quality. The first contribution focused on the contrast enhancement of the tone mapped image using a piecewise linear function as a non-uniform histogram equalization adjustment to model the "s-shaped" curve of the human visual adaptation. The second and third contributions are concerned with the details preservation of the HDR image on the tone mapped image. Separable and non-separable multiresolution approaches based on essential non-oscill...
The conversion of High Dynamic Range (HDR) image into Low Dynamic Range (LDR) image is investigated so that the visual rendering of the latter is of good quality. The first contribution focused on the contrast enhancement of the tone mapped image using a piecewise linear function as a non-uniform histogram equalization adjustment to model the "s-shaped" curve of the human visual adaptation. The second and third contributions are concerned with the details preservation of the HDR image on the tone mapped image. Separable and non-separable multiresolution approaches based on essential non-oscillatory strategies, taking into account the HDR image singularities in the mathematical model derivation, are proposed. The fourth contribution not only preserves details but also enhances the contrast of the HDR tone mapped image. A separable "near optimal" lifting scheme using an adaptive powerful prediction step is proposed. Simulation results provide good performance, both in terms of visual quality and Tone Mapped Quality Index (TMQI) metric, compared to existing competitive tone mapping approaches.
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