By Peter W. Hawkes

Advances in Imaging and Electron Physics merges long-running serials--Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. This sequence beneficial properties prolonged articles at the physics of electron units (especially semiconductor devices), particle optics at low and high energies, microlithography, photo technological know-how and electronic photograph processing, electromagnetic wave propagation, electron microscopy, and the computing tools utilized in these kinds of domain names. * Contributions from top foreign students and specialists * Discusses sizzling subject components and offers present and destiny examine tendencies * useful reference and consultant for physicists, engineers and mathematicians

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The LMSE (see Eskicioglu and Fisher, 1995), is a local measure for the difference in two images. We compute the following quantities at each pixel (with indices 2 i n À 1 and 2 j m À 1) of X and Y, respectively: Gamut Mapping 23 Lðxij Þ ¼ xðiþ1Þj þ xðiÀ1Þj þ xið jþ1Þ þ xið jÀ1Þ À 4xij and Lðyij Þ ¼ yðiþ1Þj þ yðiÀ1Þj þ yið jþ1Þ þ yið jÀ1Þ À 4yij : The image-quality measure QLMSE is then defined as QLMSE ðX; YÞ ¼ nÀ1 mÀ1 X X 1 ðLðxij Þ À Lðyij ÞÞ2 : ðn À 2Þðm À 2Þ i¼2 j¼2 Another measure considering a local contrast in images is DLC.

Another iterative approach has been proposed by McCann (2001), who describes an iterative gamut-mapping technique based on spatial image compression. The objective in McCann’s approach is to optimize ratios of radiances. In both optimization approaches, the main difficulty is defining an appropriate optimization criterion using a perceptual model. Another Gamut Mapping 31 issue is the computational efficiency that makes use of these algorithms difficult in practical applications. So far, optimization approaches respecting spatial features become so high dimensional that the objective function cannot be optimized directly.

In that case, better results can be achieved by using a linear combination of a global and a personalized model. The best weight for the linear combination can be found by testing multiple values and choosing the one with the highest mean hit rate using cross-validation. 6. GAMUT MAPPING AS AN OPTIMIZATION PROBLEM Gamut mapping can be described as a constrained optimization problem. The objective is to find a mapped image that represents an original image as best as possible, that is, we want to minimize the perceived difference of a mapped image and the original image under the constraint of color gamut limitations.

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