图像增强对低光照场景语义分割影响研究

Effect of Image Enhancement on Semantic Segmentation of Low⁃Light Scene

在低光照环境下获取的图像通常会出现图像亮度低、颜色失真、细节信息丢失以及对比度低等问题。为了满足主观视觉体验的需求,往往会对图像进行增强处理。然而,图像增强对机器视觉应用性能的影响缺乏系统研究。本文以语义分割这一机器视觉应用为例,首先对主流的语义分割方法和低光照图像增强方法进行归纳总结,然后对经图像增强方法处理的低光照图像进行语义分割,从而探究图像增强方法对低光照场景语义分割性能的影响。实验结果表明,增强处理可以改善图像的人眼视觉效果,但是可能会引入噪声等影响,并且图像增强方法和语义分割方法关注的重点和特征不完全一致。图像增强对于低光照场景语义分割性能的促进作用并不明显,甚至会带来负面影响。

Images acquired in low-light environments always suffer from low brightness,color distortion,loss of detail information,low contrast,and other problems.To meet the needs of subjective visual experience,researchers often enhance the images.However,the impact of image enhancement on the performance of machine vision applications is not systematically researched.In this paper,we first summarize typical low-light image enhancement methods and semantic segmentation methods.Next,we take a machine vision application(i.e.,semantic segmentation)as an example and select the low-light scene to investigate the effect of image enhancement methods on the semantic segmentation performance of the low-light scene.The experimental results show that enhancement processing can improve the visual effect of images,but may introduce noise.In addition,image enhancement methods and semantic segmentation methods do not concentrate exactly on the same focus and features.Therefore,image enhancement doesnot contribute significantly to the performance of semantic segmentation in low-light scenes,and even brings negative effects.