Single image super-resolution reconstruction method combining depth learning and gradient transformation

A technology of super-resolution reconstruction and deep learning, which is applied in the field of single image super-resolution reconstruction and image super-resolution reconstruction, and can solve the problem that the details and fine structure parts of the image cannot achieve the reconstruction effect, etc.

Active Publication Date: 2016-12-07
SICHUAN UNIV
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Problems solved by technology

The super-resolution method based on the gradient prior can effectively remove the ringing effect and the jagged effect of the reconstructed image, but this type of method cannot reconstruct the details and fine structures of the image very well.

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  • Single image super-resolution reconstruction method combining depth learning and gradient transformation
  • Single image super-resolution reconstruction method combining depth learning and gradient transformation
  • Single image super-resolution reconstruction method combining depth learning and gradient transformation

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Embodiment Construction

[0015] The present invention will be further described below in conjunction with accompanying drawing:

[0016] figure 1 Among them, a single image super-resolution reconstruction method combining deep learning and gradient transformation includes the following steps:

[0017] (1) Upsampling the input low-resolution image with a super-resolution method based on deep learning to obtain an upsampled image;

[0018] (2) Use the gradient operator to perform gradient extraction on the upsampled image;

[0019] (3) Transform the extracted gradients with a deep convolutional neural network;

[0020] (4) Using the input low-resolution image and the gradient obtained from step (3) conversion as a constraint to establish a reconstruction cost function;

[0021] (5) Use the gradient descent method to optimize the reconstruction cost function to obtain the final output high-resolution image.

[0022] Specifically, in the step (1), we use a deep learning-based super-resolution method t...

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Abstract

The invention discloses a single image super-resolution reconstruction method combining depth learning and gradient transformation. The method comprises the steps that a super-resolution method based on depth learning is used to carry out upsampling on an input low-resolution image to acquire an upsampling image; a gradient operator is used to carry out gradient-extracting on the upsampling image; a depth convolutional neural network is used to convert extracted gradient; a cost function is reconstructed by using the input low-resolution image and the converted gradient as constraints; a gradient descent method is used to optimize the reconstructed cost function to acquire a final output high-resolution image. According to the single image super-resolution reconstruction method provided by the invention, the reconstructed image has a fine structure in the subjective visual effect, is free of artificial effect, and has a high objective evaluation parameter value. The invention provides the effective single image super-resolution reconstruction method.

Description

technical field [0001] The invention relates to image super-resolution reconstruction technology, in particular to a single image super-resolution reconstruction method combined with deep learning and gradient transformation, belonging to the field of digital image processing. Background technique [0002] In real life, due to the limitation of imaging equipment and imaging environment, as well as the loss of image information in the transmission process, the images acquired by people are often of low resolution and low quality, which is difficult to meet the needs. Image super-resolution reconstruction technology is a technology that reconstructs input low-resolution images into high-resolution images through signal processing technology without increasing hardware costs. The image reconstructed by image super-resolution reconstruction technology is not only better than the input image in spatial resolution, but also has a significant improvement in subjective visual effect...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T5/003G06T2207/20081G06T2207/20084
Inventor 何小海陈敬勖陈洪刚滕奇志卿粼波熊淑华
Owner SICHUAN UNIV
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