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Compressed multi-scale feature fusion network-based image super-resolution reconstruction method

A multi-scale feature fusion network technology, applied in image super-resolution reconstruction, image super-resolution reconstruction based on compressed multi-scale feature fusion network, can solve high-resolution image peak signal-to-noise ratio and low structural similarity problem, to achieve the effect of improving the peak signal-to-noise ratio and structural similarity, increasing the number, and improving the fitting ability

Active Publication Date: 2018-09-14
XIDIAN UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies in the above-mentioned prior art, and propose an image super-resolution reconstruction method based on a compressed multi-scale feature fusion network, which is used to solve the reconstructed high-resolution image peaks existing in the prior art Technical issues with low signal-to-noise ratio and structural similarity

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[0034] specific implementation plan

[0035] Below in conjunction with accompanying drawing and specific embodiment, the present invention is described in further detail:

[0036] refer to figure 1 , an image super-resolution reconstruction method based on compressed multi-scale feature fusion network, including the following steps:

[0037] Step 1) Get the training sample set:

[0038]Step 1a) Extract 291 RGB images from the Berkeley segmentation database, and rotate each image, the degrees of rotation are 0°, 90°, 180°, and 270°, and then perform scale transformation on each image, and the transformation scales are respectively 1 times, 6 times, 7 times, 8 times, 9 times, get 5820 RGB images;

[0039] Step 1b) Perform format conversion on 5820 RGB images, and extract a Y-channel image from each of the obtained 5820 YCbCr images to obtain 5820 Y-channel images, and number the 5820 Y-channel images to obtain 5820 images with Numbered Y-channel images;

[0040] Step 1c) Pe...

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Abstract

The invention provides a compressed multi-scale feature fusion network-based image super-resolution reconstruction method. The invention aims to solve a technical problem that a reconstructed high resolution image has a low peak signal to noise ratio and low structural similarity in the prior art. The implementation process of the invention includes the following steps that: a training sample setcomposed of high- and low-resolution image pairs is obtained; a multi-scale feature fusion network is constructed; the multi-scale feature fusion network is trained; a compressed multi-scale feature fusion network is obtained; and the compressed multi-scale feature fusion network is adopted to perform super-resolution reconstruction on an RGB image to be reconstructed. According to the compressedmulti-scale feature fusion network-based image super-resolution reconstruction method of the invention, a plurality of multi-scale feature fusion layers which are connected with one another sequentially in a stacked manner in the multi-scale feature fusion network are adopted to extract the multi-scale features of low-resolution images, and nonlinear mapping is performed on the multi-scale features of the low-resolution images; and therefore, the improvement of the low peak signal to noise ratio and low structural similarity of the reconstructed high-resolution image can be benefitted. The method can be applied to fields such as remote sensing imaging, public safety, medical diagnosis.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image super-resolution reconstruction method, in particular to an image super-resolution reconstruction method based on a compressed multi-scale feature fusion network, which can be used in remote sensing imaging, public security, medical diagnosis and other fields. Background technique [0002] Image super-resolution refers to increasing the resolution of an image. The image super-resolution reconstruction method is a method of reconstructing a corresponding high-resolution image from an observed low-resolution image. Image super-resolution reconstruction methods are mainly divided into three categories: interpolation-based, reconstruction-based, and learning-based methods. In recent years, learning-based methods have become the main research direction of image super-resolution reconstruction methods. The main idea is to use high- and low-resolution image pairs as tra...

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Application Information

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IPC IPC(8): G06T3/40G06K9/62G06N3/04
CPCG06T3/4053G06N3/048G06N3/045G06F18/253
Inventor 邓成樊馨霞许洁李泽宇杨延华
Owner XIDIAN UNIV
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