Low-illumination images often suffer from the problems of excessive darkness and low contrast due to insufficient lighting.Quality enhancement can improve the clarity of image,making it easier for computers to recognize and process.However,high spatial correlation between adjacent pixels makes image enhancement more difficult.To address this issue,this article proposed a method for enhancing low-illumination image quality based on improved U-Net neural network.Firstly,we used convolutional networks and down-sampling to improve the U-Net neural network.Then,we divided the low-light image into two parts:reflection and illumination.Secondly,we extracted fea-tures from both parts and input them into the improved U-Net neural network to obtain a preliminary reconstructed image.Meanwhile,we enhanced the illumination part by the Retinex theory.Moreover,we integrated the reconstructed image with the enhanced illumination component.Finally,we achieved the enhancement of low-illumination image quality.The experimental results show that the proposed method can effectively improve image quality and obtain sat-isfactory enhancement effects,with less time.
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