(EN) The invention provides an image super-resolution method and system, and the method comprises the steps of enabling a to-be-processed image to serve as the input of a convolutional neural network super-resolution model, and enabling the convolutional neural network super-resolution model to be composed of four execution modules which are connected in sequence, enabling the first execution module toprocess the to-be-processed image to obtain a first processed image, enabling the second execution module to process the first processing image and output a second processing image, enabling the third execution module to process the second processed image and output a third processed image, and enabling the fourth execution module to process the third processing image and output a super-resolution image. Based on the invention, the convolutional neural network hyper-division model sets the weighted features for the to-be-processed image, through learning the weighted features, the important features in the to-be-processed image are determined, and the super-resolution processing is carried out according to the important features, so that the feature expression capability of the convolutional neural network super-resolution model is improved, and the detail quality of the super-resolution image obtained after the super-resolution processing is greatly improved.
(ZH) 本发明提供一种图像超分辨方法和系统，该方案包括：将待处理图像作为卷积神经网络超分模型的输入，卷积神经网络超分模型由四个依次连接的执行模块构成；第一执行模块对待处理图像进行处理，得到第一处理图像；第二执行模块对第一处理图像进行处理，输出包含第二处理图像；第三执行模块对第二处理图像进行处理，输出第三处理图像；第四执行模块对第三处理图像进行处理，输出超分辨率图像。基于本发明，卷积神经网络超分模型为待处理图像设置加权特征，通过对加权特征的学习，确定待处理图像中的重要特征，并依据重要特征进行超分辨处理，从而提高卷积神经网络超分模型的特征表达能力，使得超分辨处理后所得到的超分辨率图像的细节质量大大提高。
