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Defogging is an essential preprocessing technique for object detection in computer vision-based systems and has been widely used in outdoor surveillance system applications. This paper proposes an efficient defogging algorithm for both static images and videos. Considering real-time applications, the proposed defogging algorithm involves a low-cost hardware oriented design that is based on an atmospheric scattering model and dark channel prior. Compared with previous low-complexity techniques, simulation results indicated that the proposed design demonstrated superior performance in terms of quantitative and qualitative evaluations. A weighting technique and a contour preserving estimation approach are adopted alternately to refine the factors in the defogging process. Furthermore, in the atmospheric light estimation, an adjuster is applied to the video for preventing “flicker” which means that the brightness changes dramatically between two neighbor frames in the video. Hence, the proposed algorithm is suitable for video defogging applications, which have not been dealt with in previous approaches. To achieve the requirement of real-time applications for both static and dynamic images, an implementation of seven-stage very-large-scale integration architecture for the proposed algorithm is presented. By using TSMC 0.13- $\mu {\mathrm{ m}}$ technology, the design yielded a processing rate of approximately 200 Mpixels/s. [ABSTRACT FROM AUTHOR]

Real-time stereo analysis is an important research area in computer vision. In this context, we propose a stereo algorithm for an immersive video-conferencing system by which conferees at different geographical places can meet under similar conditions as in the real world. For this purpose, virtual views of the remote conferees are generated and adapted to the current view-point of the local participant. Dense vector fields of high accuracy are required in order to guarantee an adequate quality of the virtual views. Due to the usage of a wide baseline system with strongly convergent camera configurations, the dynamic disparity range is about 150 pixels. Considering computational costs, a full search or even a local search restricted to a small window of a few pixels, as it is implemented in many real-time algorithms, is not suitable for our application because processing on full-resolution video according to CCIR 601 TV standard with 25 frames per second is addressed-the most desirable as a pure software solution running on available processors without any support from dedicated hardware. Therefore, we propose in this paper a new fast algorithm for stereo analysis, which circumvents the window search by using a hybrid recursive matching strategy based on the effective selection of a small number of candidates. However, stereo analysis requires more than a straightforward application of stereo matching. The crucial problem is to produce accurate stereo correspondences in all parts of the image. Especially, errors in occluded regions and homogenous or less structured regions lead to disturbing artifacts in the synthesized virtual views. To cope with this problem, mismatches have to be detected and substituted by a sophisticated interpolation and extrapolation scheme. [ABSTRACT FROM AUTHOR]