Damaged two-dimensional code recovery method of convolutional auto-encoder in combination with binary segmentation

The invention discloses a damaged two-dimensional code recovery method of a convolutional auto-encoder in combination with binary segmentation. The method comprises the steps of preparing a training data set; constructing a deep convolutional self-encoding neural network, wherein the deep convolutional self-encoding neural network comprises an encoder, a decoder and a binary segmentation layer, wherein the decoder adopts an up-sampling part of a U-net network, and the binary segmentation layer is used for carrying out binary classification on each feature element point in a feature tensor output by the decoder according to black and white pixels; wherein the loss function adopts a cross entropy loss function; and finally training a model for image restoration. According to the method, a convolution auto-encoder, U-net, a binary segmentation layer and the like are organically combined, and finally end-to-end restoration can be carried out on fuzzy and non-uniform illumination, noise andtwo-dimensional code ima