Image Dynamic Range Enhancement in the Context of Logarithmic Models
This work provides a theoretical and practical solution for image enhancement in computer vision, though it appears incremental as it builds on existing logarithmic models.
The authors addressed the problem of identifying and obtaining a privileged image representation under variable illumination or aperture conditions using logarithmic models, resulting in two transforms for optimal dynamic range and mean dynamic range enhancement with experimental validation.
Images of a scene observed under a variable illumination or with a variable optical aperture are not identical. Does a privileged representant exist? In which mathematical context? How to obtain it? The authors answer to such questions in the context of logarithmic models for images. After a short presentation of the model, the paper presents two image transforms: one performs an optimal enhancement of the dynamic range, and the other does the same for the mean dynamic range. Experimental results are shown.