An investigation of rain streak removal models based on expert experience and deep learning

Abstract

Computer vision technology has a wide range of applications in today's society, and image rain removal is of great importance in outdoor vision capture. Today's image de-rain techniques are divided into video de-rain, and image de-rain, with the image de-rain task being more difficult than the video de-rain task due to the lack of a time factor. Current image rain removal methods are divided into three main types: filter-based methods, a priori knowledge-based methods and deep learning methods. Although these methods can achieve the image rain removal requirements to a certain extent, there is still no highly generalized method that can better solve the image rain removal problem in all cases. This paper first considers a filter-based approach, which takes less time to run but is difficult to remove cleanly for complex rain streaks. Secondly, this paper examines an a priori knowledge-based approach, which requires the study of how rain images are constructed as a priori knowledge and then uses the existing prior knowledge to remove rain from the images. This approach has a high reliance on prior knowledge and poor generalization. Finally, this paper investigates a deep learning-based method that requires a large number of supervised samples for training and has a better rain removal effect, but ignores the prior knowledge of rain streaks and is prone to overfitting. Based on these three methods we collected some data and experimental results in this field and summarized and analyzed them, giving reasons for the strengths and weaknesses of these three models and presenting new perspectives for future improvements in image de-rain methods.