An image super-resolution algorithm based on context-dependent multi-task depth learning

A deep learning, multi-task technology, applied in the field of image super-resolution algorithm, can solve the problems of inconsistency in structure, difficult for neural network to capture structural changes, unreal image details, etc., to achieve the effect of structure preservation

Active Publication Date: 2019-02-26
SUN YAT SEN UNIV
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AI Technical Summary

Problems solved by technology

Although such methods have greatly improved the quality of image super-resolution, they will also produce some structurally inconsistent defects.
The main reason is that the neural network with the cost function of minimizing the mean square error is difficult to capture the more sensitive structural cha

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  • An image super-resolution algorithm based on context-dependent multi-task depth learning
  • An image super-resolution algorithm based on context-dependent multi-task depth learning
  • An image super-resolution algorithm based on context-dependent multi-task depth learning

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Embodiment 1

[0033] A context-dependent multi-task deep learning method for static image super-resolution, comprising the following steps:

[0034] S1: collecting image data;

[0035] S2: Establish a neural network model;

[0036] S3: using the collected image data to train the established neural network model;

[0037] S4: Process the trained neural network on static low-resolution images to obtain high-resolution images.

[0038] In step S1, the training data consists of three parts, namely HR images, edge images corresponding to the HR images, and LR images corresponding to the HR images. Wherein, the edge image corresponding to the HR image is a binary image, which can be given by an edge detection algorithm, or can be given by manual marking. Pixels with a value of 0 in the edge image represent non-edges, and pixels with a value of 1 represent edges. Assuming that the resolution of the LR image is h×w and the resolution of the HR image is H×W, the trained network can only be used ...

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Abstract

The invention provides an image super-resolution algorithm based on context-related multi-task depth learning, Three depth neural networks are designed for capturing the basic information, the main edge information and the small detail information of the image, and then the neural networks are trained in a multi-task learning framework for context-related connection and unification. Given the input low-resolution image, the trained neural network will output the basic image, the main edge image and the micro-detail image respectively, and the final high-resolution image will be fused from thebasic image and the micro-detail image. The algorithm can recover high resolution (HR) images only from static low resolution (LR) images. Moreover, the structure of the recovered HR image is well preserved, and the structure information in the ideal HR image can be recovered as much as possible.

Description

technical field [0001] The present invention relates to the field of digital image processing, and more specifically, relates to an image super-resolution algorithm based on context-dependent multi-task deep learning. Background technique [0002] Image super-resolution is a technology that improves the resolution of images by computing, and can be widely used in video surveillance and medical image analysis. The problem to be solved by the single image super-resolution algorithm is how to improve the resolution of the image when the input is only a single image. This problem is a fundamental problem in the field of image processing, and it is also a pathological problem. [0003] Traditional solutions can be divided into reconstruction-based algorithms, sample-based methods, and interpolation-based methods. The reconstruction-based method is to model the relationship between the high-resolution (HR) image and the low-resolution (LR) image with a convolution kernel that ac...

Claims

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Application Information

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IPC IPC(8): G06T3/40
CPCG06T3/4053
Inventor 林倞施煜锴陈崇雨王可泽成慧
Owner SUN YAT SEN UNIV
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