Abstract: Digital media deep interactive images are affected by noise, resulting in poor edge detection performance and affecting segmentation accuracy. Therefore, a digital media deep interactive image segmentation algorithm based on edge detection is proposed. Firstly, the wavelet transform method is used to denoise images in digital media to improve the accuracy of image segmentation. Secondly, Gaussian function and low-pass filter are used to enhance the denoised image, improve the image definition, and facilitate image segmentation. Finally, based on the adaptive threshold algorithm, edge detection is performed on digital media images. There are two thresholds in the pixel collection, the upper threshold and the lower threshold. The high and low thresholds in the pixel set are calculated based on the calculation of their upper and lower thresholds, and edge connections between the two thresholds are implemented to achieve digital media image segmentation. The experimental results show that the proposed method has good denoising effect, high segmentation accuracy, and high segmentation efficiency for segmented digital media images. 

Key words:  , digital media, edge detection, deep interactive image segmentation algorithm, wavelet transform method, multi-scale retinex with color restoration(MSRCR-HIS) algorithm