Perceptually-based restoration of backlit images

Javier Vazquez-Corral, Praveen Cyriac, Marcelo Bertalmío

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3 Citas (Scopus)

Resumen

Scenes with back-light illumination are problematic when captured with a typical LDR camera, as they result in dark regions where details are not perceivable. In this paper, we present a method that, given an LDR backlit image, outputs an image where the information that was not visible in the dark regions is recovered without losing information in the already well-exposed parts of the image. Our method has three main steps: first, a variational model is minimized using gradient descent, and the iterates of the minimization are used to obtain a set of weight maps. Second, we consider the tone-mapping framework [3] that depends on four parameters. Two different sets of parameters are learned by dividing the image in the darker and lighter parts. Then, we interpolate the two sets of parameter values in as many sets as weighting maps, and tone-map the original image with each set of parameters. Finally, we merge the new tone-mapped images depending on the weighting maps. Results show that our method outperforms current backlit image enhancement approaches both quantitatively and qualitatively.

Idioma originalInglés
Título de la publicación alojadaCIC 2018 - 26th Color and Imaging Conference
Subtítulo de la publicación alojadaColor Science and Engineering Systems, Technologies, and Applications, Technical Papers and Proceedings
EditorialSociety for Imaging Science and Technology
Páginas32-37
Número de páginas6
ISBN (versión digital)9780892083374
DOI
EstadoPublicada - 2018

Serie de la publicación

NombreFinal Program and Proceedings - IS and T/SID Color Imaging Conference
Volumen2018-November
ISSN (versión impresa)2166-9635
ISSN (versión digital)2169-2629

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