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Tone mapping methods aim to display a high-dynamic-range image on a common 8-bit liquid crystal display by compressing the dynamic range. We propose a simple but effective tone mapping operator (TMO) by using localized gamma correction. As the TMO involves the contrast and the color saturation components, our idea is to make use of gamma correction to adjust the values of these two components locally. The local weights can be determined according to the pixel values within local regions. Such an adaption can be handled more effectively for the contrast and color saturation in dark and bright regions of an image. Numerical examples are given to demonstrate the merit of the localized gamma correction scheme. The experiments reported in this paper illustrate that the proposed method can provide visually pleasing results, and it is also competitive with the other testing methods. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to study image enhancement by using sparse and redundant representations of the reflectance component in the Retinex model over a learned dictionary. This approach is different from existing variational methods, and the advantage of this approach is that the reflectance component in the Retinex model can be represented with more details by the dictionary. A variational method based on the dynamic dictionaries is adopted here, where it changes with respect to iterations of the enhancement algorithm. Numerical examples are also reported to demonstrate that the proposed methods can provide better visual quality of the enhanced high-contrast images than the other variational methods, i.e., revealing more details in the low-light part. [ABSTRACT FROM AUTHOR]