Johan Schirris

Background Estimation and Adaptation Model with Light-Change Removal for Heavily Down-Sampled Video Surveillance Signals

This paper describes a background-subtraction system with light change-detection which works on a luminance QCIF-size video signal for surveillance applications. The new proposed pixel background model is controlled by a statistical threshold and is robust for cluttered background and small object motions. Moreover, (or light-change detection, we introduce temporal prediction of pixel values to estimate trends while quickly adapting to scene changes to facilitate a very sensitive detection of moving targets. Experiments show that a local contrast enhancement applied prior to down-sampling improves detection sensitivity, arid combined with the shifted sealed difference and me Wronskian determinant operators provides the best background/foreground detection.

Locally-Adaptive Image Contrast Enhancement without Noise and Ringing Artifacts

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 high-frequency 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.

Non-linear locally-adaptive video contrast enhancement algorithm without artifacts

For real-time imaging with digital video cameras and high-quality display with TV systems, visibility of details is very important to ensure user quality acceptance. 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 over-emphasizing noise. We present a multi-window real-time high-frequency enhancement scheme, in which gain is a non-linear 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. The overall quality enhancement is based on a careful selection of the filter types for the multi-band decomposition and a detailed analysis of the signal per frequency band. The advantage of the proposed technique is that details in images become more visible without introduction of annoying artifacts. The new scheme can be successfully applied to cameras and TV systems to improve their contrast.

Efficient Operator for Local Energy Measurements to Enhance an Image Signal

For real-time imaging with digital video cameras and high-quality display with TV systems, the obtained picture quality including visibility of details, local contrast, and absence of artifacts, is very important to ensure user quality acceptance. We present a multi-window real-time high-frequency enhancement scheme, in which gain is a non-linear function of the detail energy. Then we discuss the computation of commonly used local energy measurements and show that a selection of those measurements can be calculated efficiently. In our first contribution, we propose a new local energy measurement APS that can be calculated more efficiently than the existing metrics in a 2-D-separable fashion. In addition, we also show that the new APS measurement gives better performance than standard energy measurements. The second contribution is the use of local contrast and a modified contrast gain formula that can substantially improve the overall algorithm performance, especially when a high-level contrast enhancement is desired. Our algorithm trades off between added contrast and “halo” artifacts, resulting in a good balance between visibility of details and an acceptable level of artifacts. The new scheme can be successfully applied to cameras and TV systems to improve their visual quality.

Multi-band locally-adaptive contrast enhancement algorithm with built-in noise and artifact suppression mechanisms

For real-time imaging in surveillance applications, visibility of details is of primary importance to ensure customer confidence. 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 high-frequency enhancement scheme, in which gain is a non-linear function of the detail energy. Our algorithm simultaneously controls perceived sharpness, ringing artifacts (contrast) and noise, resulting in a good balance between visibility of details and non-disturbance of artifacts. The overall quality enhancement is based on a careful selection of the filter types for the multi-band decomposition and a detailed analysis of the signal per frequency band. The advantage of the proposed technique is that detail gains can be set much higher than usual and the algorithm will reduce them only at places where it is really needed.