Efficient super-resolution reconstruction method based on deep learning

Abstract

The invention relates to the field of image processing and computer vision, relates to a convolutional neural network image super-resolution reconstruction related technology, in particular to an efficient super-resolution reconstruction method based on deep learning. The reconstruction method is based on a relatively simple and efficient FSRCNN network structure in a super-resolution reconstruction field structure. A nearest neighbor interpolation algorithm, residual connection, dense connection and transfer learning are added; a new network structure, namely a LuNet structure, is formed; theLuNet structure and an existing common super-resolution algorithm (Bicubic, SCN, SRCNN, FSRCNN) are subjected to an experimental test in performance, and the LuNet structure is obtained. The test structure shows that the LuNet structure formed by the method has higher reconstruction quality and more perfect performance under the condition that the model parameter quantity is kept unchanged.

Description

technical field [0001] The present invention relates to the fields of image processing and computer vision, and relates to technologies related to convolutional neural network image super-resolution reconstruction, specifically an efficient super-resolution reconstruction method based on deep learning. Background technique [0002] Image super-resolution reconstruction, also known as image enlargement, aims to enlarge the low-resolution image input by the user while improving the clarity of the image. After the image super-resolution reconstruction process, the low-resolution image becomes clearer. Traditional super-resolution image reconstruction methods use interpolation methods such as linear interpolation, bilinear interpolation, and nearest neighbor interpolation. This processing effect is fast, but it can blur high-frequency details in the picture and cause serious distortion. [0003] With the development of convolutional neural network (CNN), a large number of conv...

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Problems solved by technology

Although this processing effect is fast, it will cause blurred high-frequency details in the picture, causing severe distortion

[0003] With the development of convolutional neural network (CNN), a large number of convolutional neural networks are used in the field of super-resolution reconstruction. CNN with complex structure can complete the reconstruction process through feature extraction and mapping of multiple modules, although it will form real detailed information. , but due to the complexity of the network, it is difficult to achieve real-time and fast super-resolution reconstruction

The CNN with a simple structure can complete the feature extraction and reconstruction process through a relatively rough module structure. Although it has high real-time performance, it will lack relatively complete high-frequency details.

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