Research On Single Image Rain And Snow Removal Methods

Computer vision system has been widely applied in intelligent transportation, public safety, border security, sports events reported and other fields. But the photos or videos capturing in the bad weather condition (such as rain, snow, haze, etc.), not only significantly degrade visual quality, but also degrade the effectiveness of any computer vision algorithm, such as tracking, recognition, retrieving and so on.Rain or snow removal in video has been made significant advances. But if there are some moving objects or dynamic background in the video, some rain or snow removal methods can’t well detect the rain streaks or snowflakes. And without temple information it is difficult to apply them to the case of single image.In many cases (such as download the picture from Internet or take a photo with a camera), only the one image can be used. Rain and snow removal methods from single image would not suffer from the question of moving objects or dynamic background. And rain or snow removal methods from single image can improve the performance of some algorithms which only suit for single image (such as image retrieval). This paper mainly aims to remove rain or snow from single image, to improve the visual quality of rainy or snowy images. The main content and achievement of this paper is as follows:1. Research rain or snow from the relation between the pixels of edges and the surrounding pixels. All edges can be classified into three categories:step edge, ridge edge and valley edge. Rain or snow in the image belong to the little ridge edges. This paper proves experimentally and theoretically the low frequency part using guided filter can explain the intensity profiles of three-type edges. And conclude that the low frequency part is non-rain or non-snow.2. Single-image-based rain and snow removal method using multi-guided filter is proposed. Because the low frequency part is non-rain or non-snow, the low frequency part could be modified as a non-rain or non-snow guidance image, while the high frequency part is treated as an input image of the guided filter, so that a non-rain component of the high frequency part can be obtained. And then add it to the low frequency part to get the recovered image. Further we take the minimization between the input image and the recovered image to restore the valley edges. Finally we use guided filter once again to get the final refined recovered image.3. A guided L0smoothing filter is designed inspired by previous Lo gradient minimization. Base on the theory of L0gradient minimization and experimental analyzes, we design a guided Lo smoothing filter. The designed filter can smooth the input image according to the gradient magnitude of guided image, but not according to the structure of guided image like previous guided filter. And from the experimental results, the designed guided Lo smoothing filter has a better performance than guided filter in the given case.4. Single image rain and snow removal method via guided L0smoothing filter is proposed. And the inputs of the designed guided L0smoothing filter are the non-rain or non-snow but blurred guidance image and observed image, while the output is a coarse no-rain or no-snow image. Because the low frequency part lost the information of valley edges, we take the minimization between the input image and the coarse no-rain or no-snow image to acquire the corresponding final result. The proposed method has better perceptual quality against the rain and snow removal method using multi-guided filter.