Core CT image super-resolution reconstruction method based on three-dimensional convolutional neural network

Abstract

The invention discloses a core three-dimensional image super-resolution method, which comprises the following steps: (1) sending an image in a training set to a three-dimensional convolutional neuralnetwork proposed by the method, wherein the first layer of the network performs low-frequency feature extraction; (2) allowing the second to eleventh layers of the network to be responsible for learning a mapping relationship between low frequency and high frequency features; (3) allowing the twelfth layer of the network to use the learned mapping relationship to map the low frequency features into the high frequency features; (4) using a residual learning method to calculate a root mean square error, and accelerating the training by using the momentum gradient descent method; (5) using the adaptive learning rate and a gradient cutting method to optimize the training process during the process of training, and using the training configurations in (1) to (5) to perform continuous iterativetraining; and (6) using the trained network model to complete the reconstruction. The invention can improve the resolution of a rock CT three-dimensional image, restore more structure and details, andprovide clearer image samples for the next step of geology-petroleum research.

Description

technical field [0001] The invention relates to an image restoration and enhancement method, in particular to a rock core three-dimensional image super-resolution method, and belongs to the technical field of three-dimensional image super-resolution reconstruction. Background technique [0002] In the process of petroleum geological analysis, it is usually necessary to obtain a three-dimensional core structure to study the physical properties of rocks, and to analyze the seepage properties and migration laws in rock reservoirs. CT (Computed Tomography, computerized tomography) is a three-dimensional imaging technology that can provide high-quality images of internal structures of objects, and is widely used in medical image diagnosis and geological exploration. However, in production and life, the resolution of CT images is restricted by scanning equipment and actual conditions. Usually, to obtain higher-precision CT images, it takes longer scanning time. Due to the extensi...

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Dong C, Chen C L, He K, etal.(Image Super-Resolution Using Deep Convolutional Networks[J].IEEE Transactions on Pattern Analysis&Machine Intelligence 2016,38(2):295-307) Introducing Convolutional Neural Ne

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