Research On Crop Rain Map Restoration Based On A Kind Of Multi-stage Network Model

In the presence of heavy rains,the atmosphere is often filled with water droplets and dust,resulting in image degradation such as color attenuation,blurring,and low contrast.Such degradation poses significant challenges to computer vision tasks,including object recognition,classification,and many others.In the digital age,the restoration and enhancement of degraded images have a crucial influence on the effectiveness and development of downstream machine vision tasks.Therefore,removing rain streaks from rainy images has attracted considerable attention in recent years.This thesis reviews the domestic and foreign state of research in rain streak removal algorithms and identifies their limitations in removing dense rain streaks.Most existing methods do not start from the different components of noise and fail to account for the complex relationship between different noises in a single image representation.To overcome these challenges,this thesis proposes a one-stage enhanced rain streak removal algorithm based on a one-stage rain removal method with two branches.This approach is then optimized to a multi-stage enhanced rain streak removal algorithm.The main research contents of this thesis are summarized as follows:1.The thesis first analyzes the imaging mechanism of rainy images and the composition and classification of rain streak noise.It proposes that not only rain and fog can be divided into additive and multiplicative component,but also rain streak noise can be divided into additive and multiplicative noise under different conditions.2.This thesis analyzes the design principles and advantages of the branch network by enumerating the single image deraining network.Branch network is not only the refinement of task,but also the synergistic effect of two networks to strengthen the network effect.Based on this design idea,a one-stage enhanced rain streak removal algorithm is designed.The new model deals with additive noise and multiplicative noise separately,and combines the attention module to obtain certain enhancement effect.3.For the different characteristics of single-scale processing and multi-scale processing,a multi-stage design is adopted based on the one-stage enhanced rain streak removal network,which can generate context-reliable and spatially fine output.At the same time,cross-stage transmission is employed to make full use of the crucial information of each stage for rainy image restoration.The network fully combines with a variety of network advantages,and achieves obvious performance improvement.The thesis presents a quantitative and qualitative analysis of the two rain streak removal networks proposed and demonstrates through experiments that the proposed method outperforms existing approaches.It can produce images with better background details and clarity,and achieve a clearer and more natural level of processing.