Rain Removal Using Kalman Filter in Video

In outdoor environment vision system, weather effects are complex noise. They degrade the performance of vision algorithm such as feature detection, segmentation and object recognition. Rain scene has property that an image pixel is never always covered by rain throughout the whole video. Even though we can only see a short distance ahead because of heavy rain, each pixel of the scenes taken by camera is not always covered by rain. If pixel is affected by rain, this pixel has large intensity but this pixel has original chromatic information. Consequently we make a recovery to original color value via decreasing intensity to original value. Proposing recursive data processing, we can estimate the intensity value of each pixel. And this method needs the periodic reset. If unsuitable estimated mean value is determined because of heavy noise, henceforth cleanly captured scene has little weight effect for estimated mean value. Accordingly we reset the weighted value between estimated intensity and new captured one. Additionally, the proposed method has big advantages, including small equation by repeatedly estimating the mean of intensity and handling in real-time and benefits that too simple and it can implement to real time processing. And we obtain more pure scene than existing methods.