The quality of photos is highly susceptible to severe weather such as heavy rain;it can also degrade the performance of various visual tasks like object detection.Rain removal is a challenging problem because rain str...The quality of photos is highly susceptible to severe weather such as heavy rain;it can also degrade the performance of various visual tasks like object detection.Rain removal is a challenging problem because rain streaks have different appearances even in one image.Regions where rain accumulates appear foggy or misty,while rain streaks can be clearly seen in areas where rain is less heavy.We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network(MSGMFFNet).Specially,to deal with rain streaks,our method generates a rain streak attention map,while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation.Using these tools,the model can restore a result with abundant details.Furthermore,a hybrid multiscale loss combining L1 loss and edge loss is used to guide the training process to pay attention to edge and content information.Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.展开更多
基金This work was supported in part by the National Key R&D Program of China under No.2017YFB1003000the National Natural Science Foundation of China under No.61872047 and No.61720106007+2 种基金the Beijing Nova Program under No.Z201100006820124the Beijing Natural Science Foundation(L191004)the 111 Project(B18008).
文摘The quality of photos is highly susceptible to severe weather such as heavy rain;it can also degrade the performance of various visual tasks like object detection.Rain removal is a challenging problem because rain streaks have different appearances even in one image.Regions where rain accumulates appear foggy or misty,while rain streaks can be clearly seen in areas where rain is less heavy.We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network(MSGMFFNet).Specially,to deal with rain streaks,our method generates a rain streak attention map,while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation.Using these tools,the model can restore a result with abundant details.Furthermore,a hybrid multiscale loss combining L1 loss and edge loss is used to guide the training process to pay attention to edge and content information.Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.
