An extreme low-light imaging enhancement method based on a multi-residual attention shrinkage network
Extreme-low light imaging enhancement can provide observers with near-daylight visual experience in extremely dark environments,and plays a vital role in many civil and military applications.Images and videos taken in ultra-low light level environment usually have inherent defects,such as extremely low brightness and contrast,high noise level,and serious lack of scene details and colors.In recent years,deep learning has brought new opportunities for the research of ultra-low light level imaging.In this paper,we collect and provide a series of more practical ultra-low light level training data sets,and propose a multiple residual attention shrinkage network.Thus,a new ultra-low light level imaging method is developed.The prospect of industrial mass production of this method is confirmed by the successfully developed miniaturized prototype.This paper implements the residual internal attention mechanism based on channel attention and spatial attention,as well as the external attention mechanism based on depth soft threshold shrinkage.This approach can not only effectively extract and restore the image detail information under extremely low illumination environment,restore the true color of the scene,but also effectively remove the huge amount of noise caused by insufficient light sensitivity of imaging equipment in such environment.The measured results show that this method can effectively enhance the extremely low illumination environment and has high real-time performance.Compared with the latest methods in the industry,the proposed method is superior in terms of subjective visual experience and objective parameters.
万方数据
维普
