A computer-implemented image processing method, comprising: acquiring, by a Computerized-Tomography scanning system, projection domain data of an object; and processing, by using a convolutional neural network, the projection domain data, to acquire an estimated image of the object; wherein the convolutional neural network comprises: a projection domain network for processing input projection domain data to obtain processed projection domain data; an analytical reconstruction network layer for performing analytical reconstruction on the processed projection domain data to obtain a reconstructed image; and an image domain network for processing the reconstructed image to obtain an estimated image, wherein the method further comprises training the convolutional neural network by: adjusting the image domain network by using an a priori model cost function based on the estimated image, and performing back propagation of a gradient through the analytical reconstruction network layer to adjust parameters of a convolutional kernel of the projection domain network; and performing a projection operation on the estimated image by using a system projection matrix of the CT scanning system to obtain a projection result, and adjusting parameters of convolutional kernels of the image domain network and the projection domain network by using a consistency cost function based on the projection result, the estimated image, and a likelihood function reflecting a probability distribution of the projection domain data. acquiring, by a Computerized-Tomography scanning system, projection domain data of an object; and processing, by using a convolutional neural network, the projection domain data, to acquire an estimated image of the object; wherein the convolutional neural network comprises: a projection domain network for processing input projection domain data to obtain processed projection domain data; an analytical reconstruction network layer for performing analytical reconstruction on the processed projection domain data to obtain a reconstructed image; and an image domain network for processing the reconstructed image to obtain an estimated image, wherein the method further comprises training the convolutional neural network by: adjusting the image domain network by using an a priori model cost function based on the estimated image, and performing back propagation of a gradient through the analytical reconstruction network layer to adjust parameters of a convolutional kernel of the projection domain network; and performing a projection operation on the estimated image by using a system projection matrix of the CT scanning system to obtain a projection result, and adjusting parameters of convolutional kernels of the image domain network and the projection domain network by using a consistency cost function based on the projection result, the estimated image, and a likelihood function reflecting a probability distribution of the projection domain data. a projection domain network for processing input projection domain data to obtain processed projection domain data; an analytical reconstruction network layer for performing analytical reconstruction on the processed projection domain data to obtain a reconstructed image; and an image domain network for processing the reconstructed image to obtain an estimated image, wherein the method further comprises training the convolutional neural network by: adjusting the image domain network by using an a priori model cost function based on the estimated image, and performing back propagation of a gradient through the analytical reconstruction network layer to adjust parameters of a convolutional kernel of the projection domain network; and performing a projection operation on the estimated image by using a system projection matrix of the CT scanning system to obtain a projection result, and adjusting parameters of convolutional kernels of the image domain network and the projection domain network by using a consistency cost function based on the projection result, the estimated image, and a likelihood function reflecting a probability distribution of the projection domain data. adjusting the image domain network by using an a priori model cost function based on the estimated image, and performing back propagation of a gradient through the analytical reconstruction network layer to adjust parameters of a convolutional kernel of the projection domain network; and performing a projection operation on the estimated image by using a system projection matrix of the CT scanning system to obtain a projection result, and adjusting parameters of convolutional kernels of the image domain network and the projection domain network by using a consistency cost function based on the projection result, the estimated image, and a likelihood function reflecting a probability distribution of the projection domain data.