Automatic Segmentation Algorithm for Low-Light Images Based on Improved Neural Networks
Under low-light conditions,images suffer from low contrast,blurred details,and substantial noise interference.To achieve neater edges and more complete segmentation targets in low-light images,an automatic segmentation algorithm for low-light images based on an improved neural network is proposed.The SLIC method is employed to perform superpixel segmentation on low-light images.Subsequently,all generated superpixel blocks are fused based on the pixel similarity within the low-light images.Furthermore,by incorporating an im-proved neural network,the target image and background image of the low-light image are segmented,enabling automatic segmentation of the low-light image.The experimental results demonstrate that the proposed method ex-hibits a good superpixel fusion effect for low-light images,achieves high segmentation accuracy,and is suitable for automatic segmentation tasks under low light conditions.
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