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For real-time imaging in surveillance applications, visibility of details is of primary importance to ensure customer confidence. Additional constraints are the absence of human interaction and low computational complexity. Usually, image quality is improved by enhancing contrast and sharpness. Many complex scenes require local contrast improvements that should bring details to the best possible visibility. However, local enhancement methods mainly suffer from ringing artifacts and noise over-enhancement. In this paper, we present a new multi-window real-time highfrequency enhancement scheme, in which gain is a nonlinear function of the detail energy. Our algorithm controls perceived sharpness, ringing artifacts (contrast) and noise, resulting in a good balance between visibility of details and non-disturbance of artifacts. Its advantage is that gains can be set now much higher than usual and the algorithm will reduce them only at places where it is really needed.

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Background estimation and adaptation model with light-change removal for heavily cown-sampled video surveillance signals. In Proceedings of the

No abstract