Super-resolution image reconstruction method and system based on recursive extremely deep network

A technology of super-resolution images and low-resolution images, which is applied in the field of image processing, can solve the problems of accessing useful information and low image resolution, and achieve the effect of increasing image resolution and improving model effects

Pending Publication Date: 2020-05-12
SUZHOU UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] For this reason, the technical problem to be solved by the present invention is to overcome the problem in the prior art that the previous layer of the network cannot access useful information from the latter layer, resulting in l

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  • Super-resolution image reconstruction method and system based on recursive extremely deep network
  • Super-resolution image reconstruction method and system based on recursive extremely deep network
  • Super-resolution image reconstruction method and system based on recursive extremely deep network

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Example Embodiment

[0021] Example one:

[0022] Such as figure 1 As shown, this embodiment provides a super-resolution image reconstruction method based on a recursive very deep network, which includes the following steps: Step S1: Set a training set, and perform data enhancement on the training set; Step S2: Use the training Set to train the neural network model that has been built; Step S3: Use the trained neural network model to reconstruct on the test image.

[0023] In the super-resolution image reconstruction method based on the recursive very deep network of this embodiment, in the step S1, a training set is set, data enhancement is performed on the training set, and the data is preprocessed; the step S2 In, the training set is used to train the neural network model that has been built, thereby introducing the idea of ​​recursion, so that the previous layer of the network can use the useful information of the next layer to achieve the purpose of using high-level information to improve low-lev...

Example Embodiment

[0037] Example two

[0038] Based on the same inventive concept, this embodiment provides a super-resolution image reconstruction system based on a recursive very deep network. The principle of solving the problem is similar to the super-resolution image reconstruction method based on the recursive very deep network. No longer.

[0039] Such as image 3 As shown, the super-resolution image reconstruction system based on the recursive extremely deep network in this embodiment includes:

[0040] The image preprocessing module sets a training set, and performs data enhancement on the training set;

[0041] A model training module, which uses the training set to train the neural network model that has been built;

[0042] The super-resolution reconstruction module uses the trained neural network model to reconstruct on the test image.

[0043] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program p...

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Abstract

The invention relates to a super-resolution image reconstruction method and system based on a recursive extremely deep network, and the method comprises the steps of setting a training set, and carrying out the data enhancement of the training set; utilizing the training set to train a built neural network model; and reconstructing the test image by using the trained neural network model. According to the invention, the useful information of the next layer can be used by the previous layer of the convolutional network, and the purpose of perfecting the low-level information by using the high-level information is achieved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a super-resolution image reconstruction method and system based on a recursive very deep network. Background technique [0002] Image super resolution (super resolution, SR for short) refers to the process of restoring high resolution (high resolution, HR for short) images from low resolution (low resolution, LR for short) images, which is an important technology in computer vision and image processing. Means, with a wide range of practical applications, such as medical imaging, security monitoring and remote sensing images and other fields. In addition to improving the perceived quality of images, it can also help improve other computer vision tasks. Since the image super-resolution is very challenging, it is an ill-posed inverse problem since there are cases where multiple HR images correspond to a single LR image. [0003] The image super-resolution methods ...

Claims

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Application Information

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IPC IPC(8): G06T3/40G06N3/04G06N3/08
CPCG06T3/4053G06N3/08G06N3/045
Inventor 张莉徐石王邦军周伟达
Owner SUZHOU UNIV
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