| 总结: | In the field of visual perception, the edges of images tend to be rich in effective visual stimuli, which contribute to the neural network’s understanding of various scenes. Image smoothing is an image processing method used to highlight the wide area, low-frequency components, main part of the image or to suppress image noise and high-frequency interference components, which could make the image’s brightness smooth and gradual, reduce the abrupt gradient, and improve the image quality. At present, there are still problems such as easy blurring of the edges of the image, poor overall smoothing effect, obvious step effect, and lack of robustness to noise on image smoothing. Based on the convolutional neural network, this article proposes a method for edge detection and deep learning for image smoothing. The results show that the research method proposed in this article solves the problem of edge detection and information capture better, significantly improves the edge effect, and protects the effectiveness of edge information. At the same time, it reduces the signal-to-noise ratio of the smoothed image and greatly improves the effect of image smoothing.
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