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Image super-resolution method based on densely linked neural network, storage medium and terminal

A neural network and super-resolution technology, applied in the field of medical image and satellite image processing, can solve problems such as complex network speed, achieve good image super-resolution effect, avoid repeated extraction, and improve the effect.

Inactive Publication Date: 2019-03-29
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0007] The purpose of the present invention is to overcome the deficiencies of the prior art, provide an image super-resolution method, storage medium and terminal based on a densely linked neural network, and solve the problems of complex and slow networks in the prior art

Method used

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  • Image super-resolution method based on densely linked neural network, storage medium and terminal
  • Image super-resolution method based on densely linked neural network, storage medium and terminal
  • Image super-resolution method based on densely linked neural network, storage medium and terminal

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Embodiment 1

[0049] The image super-resolution method based on the densely linked neural network provided in this embodiment has important application value in the fields of monitoring equipment, satellite images, and medical imaging. The method is used to reconstruct a corresponding high-resolution image from an observed low-resolution image, and is based on a single low-resolution reconstruction method, which solves the problem of complex and slow network in the prior art.

[0050] Such as figure 1 As shown, the method includes the following steps:

[0051] A: Image preprocessing. In this embodiment, a picture data set is required as the training set of the network. Through this step, the size of the read input image is 28x28, and the generated high-resolution image corresponds to twice the size, that is, 56x56. Specifically, this step mainly includes:

[0052] A1: Randomly cut the training image to obtain the corresponding high-resolution image Label.

[0053] Since the calculation ...

Embodiment 2

[0115] Based on the realization of Embodiment 1, this embodiment also provides a storage medium on which computer instructions are stored, and when the computer instructions are run, the image super-resolution method based on a densely linked neural network described in Embodiment 1 is executed. step.

[0116]Based on this understanding, the technical solution of this embodiment is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium. Several instructions are included to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-OnlyMemory, ROM), random access memory (RandomAccessMemory, RA...

Embodiment 3

[0118] Based on the implementation of Embodiment 1, this embodiment also provides a terminal, including a memory and a processor, the memory stores computer instructions that can be run on the processor, and when the processor runs the computer instructions The steps of the image super-resolution method based on the densely linked neural network described in Embodiment 1 are performed.

[0119] Each functional unit in the embodiments provided by the present invention may be integrated into one processing unit, or each unit may physically exist separately, or two or more units may be integrated into one unit.

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Abstract

The invention discloses an image super-resolution method based on a densely linked neural network, a storage medium and a terminal. The method includes: preprocessing an image; performing Feature extraction: Building a dense-linked neural network, inputting the low-resolution image Input from the entrance of the dense-linked neural network, and extracting the feature information contained in the Input after calculation; Predicting the super-resolution image and updating the network parameters: performing upsampling / deconvolution on the feature-extracted image to obtain the predicted image predict; calculating the error values between the predicted image predict and the real image label, and updating the parameters of the densely linked neural network in the reverse direction; and performing super resolution reconstruction. The method can remarkably improve the ability of extracting the low-frequency and high-frequency features of an image by a depth neural network, improve the effect of the image super-resolution, and improve the ability of providing information by a picture, so that the invention is applied in the field of expecting to obtain a high-resolution image and providingmore details by the picture.

Description

technical field [0001] The invention relates to the field of medical image and satellite image processing, in particular to an image super-resolution method, storage medium and terminal based on a dense link neural network. Background technique [0002] Super-resolution technology SR (Super-Resolution) refers to reconstructing corresponding high-resolution images from observed low-resolution images, which has important application value in the fields of monitoring equipment, satellite images, and medical imaging. SR can be divided into two categories: reconstructing high-resolution images from multiple low-resolution images and reconstructing high-resolution images from a single low-resolution image. SR based on deep learning is mainly based on a single low-resolution reconstruction method, namely Single Image Super-Resolution (SISR). [0003] SISR is an inverse problem. For a low-resolution image, there may be many different high-resolution images corresponding to it. Ther...

Claims

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Application Information

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IPC IPC(8): G06T3/40G06N3/04G06N3/08
CPCG06N3/084G06T3/4053G06N3/048G06N3/045
Inventor 匡平马霆松王豪爽郭雯霞陈鹏彭亮
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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