A New Algorithm of Rain (Snow) Removal in Video

Abstract

The images acquired by outdoor vision system in the rain or snow have low contrast and are blurred, and it can cause serious degradation. Traditional rain (snow) removal method is restricted with the intensity, so the effect is not ideal. According to the characteristic of vision system acquiring multiple different degraded images in a short time, the paper processes multiple images to realize restoration. Snow and rain have the dynamic characteristic that the direction, intensity and shape of rain and snow are unfixed, which makes it difficult to establish unified physical model in the spatial domain. But analyzing them in the frequency domain doesn’t affected by the dynamic characteristic. From the perspective of frequency domain, the paper uses the method of wavelet multi-level decomposition and wavelet fusion to determine the number of layers of rain (snow) noise, formulates a fusion rule based on rain (snow) noise pollution, and makes wavelet fusion on specific layer of multiple continuous degraded images for achieving the objective of rain (snow) removal. Simulation results indicated that the method in the paper not only has ideal restoration results, but also is not restricted by noise intensity.