Image super-resolution enhancement method based on bidirectional recurrent convolutional neural network

Abstract

The invention discloses an image super-resolution enhancement method based on a bidirectional recursion convolution nerve network. The method comprises two parts, i.e., model training and resolution enhancement. The model training comprises: obtaining multiple groups of high and low-resolution images comprising the completely same content, extracting high and low-resolution image block sequences through a window sliding method, and accordingly, training a bidirectional recursion convolution nerve network model. The resolution enhancement comprises: segmenting low-resolution images to be processed into a group of low-resolution image blocks to be processed through the window sliding method, generating corresponding high-resolution image blocks through the well trained bidirectional recursion convolution nerve network model, and generating corresponding high-resolution images through fusion. According to the method, space position relations of adjacent image blocks are learnt by full use of the bidirectional recursion convolution nerve network, and accordingly, the high-resolution images comprising detail information such as more edge textures and the like are obtained.

Description

technical field [0001] The invention relates to the fields of digital image processing, machine learning, and computer vision, in particular to an image super-resolution enhancement method based on a bidirectional recursive convolutional neural network. Background technique [0002] With the popularity of digital imaging devices such as cameras and mobile phones, digital images play an increasingly important role in the way humans receive information. Image resolution refers to the number of pixels contained in a digital image. The higher the resolution, the more detailed information the image contains. However, limited by imaging hardware devices with limited resolution, people cannot obtain very high-resolution images. Therefore, how to improve the resolution of existing low-resolution images has great practical application value. Image super-resolution enhancement refers to the process of generating a high-resolution image from an input single or multiple sequences of l...

AI Technical Summary

Problems solved by technology

However, limited by imaging hardware devices with limited resolution, people cannot obtain very high-resolution images

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