A method, executed by a processor, for reconstructing a Computed Tomography, CT, image, comprising: performing CT scanning on an object to be inspected to obtain projection data on a first resolution; generating projection data on a plurality of other resolutions from the projection data on the first resolution; processing, on each resolution, projection data on the corresponding resolution by using a first convolutional neural network to obtain processed projection data, and performing a back-projection operation on the processed projection data to obtain a CT image on the corresponding resolution; processing, on each resolution, a CT image on the corresponding resolution by using a second convolutional neural network; and fusing CT images on the plurality of resolutions to obtain a reconstructed image of the object to be inspected , wherein the first convolutional neural network comprises at least three convolutional layers, each of which has an activation function for performing a non-linear operation on projection data processed by the convolutional layer, and the CT image on each resolution is locally smoothed by using the second convolutional neural network, wherein either, the CT scanning is one of the following: detector under-sampling scanning, sparse-angle scanning, intra-reconstruction scanning, finite angle scanning, and linear trajectory scanning, and the first convolutional neural network is a pooling layer-free convolutional neural network; or alternatively, the CT scanning is circular scanning or helical scanning, and the first convolutional neural network further comprises a plurality of pooling layers disposed after respective convolutional layers, and a fully connected layer. performing CT scanning on an object to be inspected to obtain projection data on a first resolution; generating projection data on a plurality of other resolutions from the projection data on the first resolution; processing, on each resolution, projection data on the corresponding resolution by using a first convolutional neural network to obtain processed projection data, and performing a back-projection operation on the processed projection data to obtain a CT image on the corresponding resolution; processing, on each resolution, a CT image on the corresponding resolution by using a second convolutional neural network; and fusing CT images on the plurality of resolutions to obtain a reconstructed image of the object to be inspected , wherein the first convolutional neural network comprises at least three convolutional layers, each of which has an activation function for performing a non-linear operation on projection data processed by the convolutional layer, and the CT image on each resolution is locally smoothed by using the second convolutional neural network, wherein either, the CT scanning is one of the following: detector under-sampling scanning, sparse-angle scanning, intra-reconstruction scanning, finite angle scanning, and linear trajectory scanning, and the first convolutional neural network is a pooling layer-free convolutional neural network; or alternatively, the CT scanning is circular scanning or helical scanning, and the first convolutional neural network further comprises a plurality of pooling layers disposed after respective convolutional layers, and a fully connected layer. <EOS>