Valueless image removing method based on deep convolutional neural networks

The invention relates to a valueless image removing method based on deep convolutional neural networks. The valueless image removing method comprises the steps of firstly, after performing whitening preprocessing on an image sample set, performing pre-training on a sparse autocoder to obtain the initialization results of deep convolutional network parameters, secondly, building a plurality of layers of deep convolutional neural networks and optimizing the network parameters layer by layer, and finally, classifying a plurality of classes of problems by use of a realized multi-classification softmax model and then realizing the removal of valueless images. Due to the automatic image learning characteristic of the sparse autocoder, the classification correction rate of the valueless image removing method based on the deep convolutional neural networks is increased. The plurality of layers of deep convolutional neural networks are built on the basis of the automatic image learning characteristic of the sparse aut