Multi-scale image depth fusion method based on superpixel-level convolutional neural network

Abstract

In order to improve the image quality, reduce the image noise and improve the image definition, the image depth fusion processing is realized by using the sp CNN network (Super pixel level convolution neural network, sp CNN). The improved non-local mean method is used to de-noise the image to highlight the role of the center pixel of the image block; the de-noised image is segmented by the improved CV model (Chan-Vese, CV), and the globally optimal multi-scale image segmentation result is obtained after optimization; From the perspective of regional features, the similarity measurement of image regions is carried out to realize image preprocessing. The sp-CNN network is constructed, and with the help of the idea of pyramid pooling, the average pooling is used to extract the features of each layer from the global and local levels of the convolutional features, and the training data set is generated for training, thereby realizing multi-scale image fusion. The experimental results show that the optimal value of the root mean square error index of the proposed method is 0.58. The optimal value of structural similarity index is 41.22. On the average slope index, the optimal value is 21.39. The optimal value of cross entropy index is 2.21. This shows that the proposed method has high image definition and good visual effect, which verifies the effectiveness of the method.