Under snowy weather conditions, cameras are prone to the interference of snow and can severely reduce the quality of the captured images, which will affect the computer vision performance greatly. Since no temporal information can be exploited, snow removal from single image is a challenging problem. In this paper, a novel snow removal method from single image was proposed by designing a kind of multi-scale image processing framework both in the spatial and frequency domain. Firstly, the input snowy image was decomposed into detailed sub-images and approximate parts by the Laplacian pyramid transform. Secondly, the approximate part is decomposed again into the background and detailed sub-image by the edge-preserving and structure-preserving image smoothing filter. After that, the non-subsampled shearlet transform was introduced to detect snowflakes within the frequency domain of the detailed sub-images, while mathematical morphological filtering was adopted to remove the labeled snowflakes within their spatial domain. Finally, the desnowing image was obtained by the inverse Laplacian pyramid transform. Experiments on real-world snowy images show that the proposed method produces better results than those of other state-of-the-art methods.
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