Image reconstruction method and device, apparatus, and non-transitory computer-readable storage medium

An image reconstruction device, comprising:

• a first determination module, configured to determine norms of convolution kernels of each convolutional layer of a deep neural network model;
• a second determination module, configured to determine the convolution kernels with norms greater than or equal to a preset threshold in each convolutional layer to obtain a target convolution kernel set of each convolutional layer, wherein the target convolution kernel set of each convolutional layer comprises the convolution kernels with norms greater than or equal to the preset threshold;
• a processing module, configured to process an input image of each convolutional layer by using the convolution kernels in the target convolution kernel set of each convolutional layer respectively, to obtain a first image processing result for the deep neural network model;
• an acquisition module, configured to obtain a second image processing result by performing interpolation on the initial image;
• and
• a third determination module, configured to determine a fusion result according to the first image processing result and the second image processing result to reconstruct the initial image according to the fusion result.

An image reconstruction apparatus, comprising a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, causes the processor to perform the image reconstruction method comprising:

• determining norms of convolution kernels of each convolutional layer of a deep neural network model;
• determining the convolution kernels with norms greater than or equal to a preset threshold in each convolutional layer to obtain a target convolution kernel set of each convolutional layer, wherein the target convolution kernel set of each convolutional layer comprises the convolution kernels with norms greater than or equal to the preset threshold;
• processing an input image of each convolutional layer by using the convolution kernels in the target convolution kernel set of each convolutional layer respectively, to obtain a first image processing result for the deep neural network model;
• obtaining a second image processing result by performing interpolation on an initial image;
• and
• determining a fusion result according to the first image processing result and the second image processing result and reconstructing the initial image according to the fusion result.

A non-transitory computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the image reconstruction method comprising:

• determining norms of convolution kernels of each convolutional layer of a deep neural network model;
• determining the convolution kernels with norms greater than or equal to a preset threshold in each convolutional layer to obtain a target convolution kernel set of each convolutional layer, wherein the target convolution kernel set of each convolutional layer comprises the convolution kernels with norms greater than or equal to the preset threshold;
• processing an input image of each convolutional layer by using the convolution kernels in the target convolution kernel set of each convolutional layer respectively, to obtain a first image processing result for the deep neural network model;
• obtaining a second image processing result by performing interpolation on an initial image;
• and
• determining a fusion result according to the first image processing result and the second image processing result and reconstructing the initial image according to the fusion result.