30 April 2015 Noise removal and real-time detail enhancement of high-dynamic-range infrared images with time consistency
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Proceedings Volume 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015; 95340R (2015) https://doi.org/10.1117/12.2182896
Event: The International Conference on Quality Control by Artificial Vision 2015, 2015, Le Creusot, France
Abstract
This paper presents a noise removal and image detail enhancement method that accounts for the limitations on human's perception to effectively visualize high-dynamic-range (HDR) infrared (IR) images. In order to represent real world scenes, IR images use to be represented by a HDR that generally exceeds the working range of common display devices (8 bits). Therefore, an effective HDR mapping without losing the perceptibility of details is needed. To do so, we introduce the use of two guided filters (GF) to generate an accurate base and detail image component. A plausibility mask is also generated from the combination of the linear coefficients that result from each GF; an indicator of the spatial detail that enables to identify those regions that are prominent to present noise in the detail image component. Finally, we filter the working range of the HDR along time to avoid global brightness fluctuations in the final 8 bit data representation, which results from combining both detail and base image components using a local adaptive gamma correction (LAGC). The last has been designed according to the human vision characteristics. The experimental evaluation shows that the proposed approach significantly enhances image details in addition to improving the contrast of the entire image. Finally, the high performance of the proposed approach makes it suitable for real word applications.
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Frederic Garcia, Frederic Garcia, Cedric Schockaert, Cedric Schockaert, Bruno Mirbach, Bruno Mirbach, } "Noise removal and real-time detail enhancement of high-dynamic-range infrared images with time consistency", Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 95340R (30 April 2015); doi: 10.1117/12.2182896; https://doi.org/10.1117/12.2182896
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