No abstract

In this paper, a novel approach for noise reduction and enhancement of extremely low-light video is proposed. For noise removal, a motion adaptive temporal filtering based on a Kalman structured updating is presented. Dynamic range of denoised video is increased by adjustment of RGB histograms using Gamma correction with adaptive clipping thresholds. Finally, residual noise is removed using a nonlocal means (NLM) denoising filter. The proposed method works directly on the color filter array (CFA) raw video for achieving low memory consumption 1 .Index Terms -Noise reduction, tone mapping, nonlocal means, and low-light video. Minjae Kim is with the

1Images captured under hazy conditions have low contrast and poor color. This is primarily due to air-light which degrades image quality according to the transmission map. The approach to enhance these hazy images we introduce here is based on the 'Dark-Channel Prior' method with image refinement by the 'Weighted Least Square' based edge-preserving smoothing. Local contrast is further enhanced by multi-scale tone manipulation. The proposed method improves the contrast, color and detail for the entire image domain effectively. In the experiment, we compare the proposed method with conventional methods to validate performance.

No abstract

In this paper, an effective top-view people detection algorithm based on multiple subarea models is proposed for smart home system. Conventional single model based detector is difficult to achieve high performance in top-view people detection since there are too many possible individual poses in the top-view based image scene and it is impossible to cover all the poses with single model. Therefore, this paper develops a model of 9 typical poses to mitigate the low detection performance problem of conventional method. Moreover, by restricting the local scope of every pose model, the proposed approach yields an improved detection rate while reducing false alarm compared to the conventional single model based detector.