Training method and device of image super-division model and computer readable storage medium

Abstract

The invention provides a training method and device for an image super-division model. The training method comprises the steps of forming an initial model which comprises a super-division module composed of a plurality of information distillation sub-modules which are sequentially connected end to end, wherein the information distillation sub-module comprises a plurality of residual units which are sequentially connected end to end; inputting the low-resolution image into the initial model, and extracting shallow layer features of the low-resolution image; extracting the deep features from the shallow features through a super-division module; summing and fusing the shallow-layer features and the deep-layer features, and then performing up-sampling processing to obtain a super-resolution image output by the initial model; and training the initial model according to a loss function determined by the super-resolution image and the high-resolution image to obtain an image super-resolution model. According to the method, refined features can be extracted by using an information multi-distillation structure based on a residual unit, and superposing shallow features with the refined features, so that the problems of degradation and gradient generated when a model network is in a deep layer are solved, and the parameter quantity and the calculation quantity are greatly reduced.

Description

technical field [0001] The present application belongs to the field of computer technology, and in particular relates to a training method and device for an image super-resolution model, an image super-resolution method and device, and a computer-readable storage medium. Background technique [0002] Functions such as special effect cameras and shooting props have become standard configurations for video applications. However, due to the limitation of equipment computing power, the on-end model only allows inference at a small image resolution, resulting in blurred images. Therefore, super resolution is required. Algorithms to improve the overall image effect. [0003] In related technologies, a trained convolutional neural network model can be used to perform super-resolution processing on an original image to obtain a super-resolution image with higher definition. [0004] However, in the current solution, the convolutional neural network model has a large amount of calcu...

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Problems solved by technology

[0005] In view of this, this application provides a training method, device, and computer-readable storage medium for an image super-resolution model, which solves the calculation, parameter, and memory read/write volumes of the convolutional

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