Reference image guided super-resolution method based on dense matching and adaptive fusion

A dense matching and super-resolution technology, applied in the field of computer vision, can solve the problems that the reference image information cannot be fully utilized, non-rigid transformation cannot be realized, and the computational complexity is high.

Active Publication Date: 2020-11-10
TIANJIN UNIV
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Problems solved by technology

This algorithm can search for the most similar image block in the reference image to restore the details of the low-resolution image, but the disadvantage is that the calculation complexity is high, and it cannot realize non-rigid transformation, so that the information of the reference image cannot be fully utilized.
[0004] Another way to achieve no...

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  • Reference image guided super-resolution method based on dense matching and adaptive fusion
  • Reference image guided super-resolution method based on dense matching and adaptive fusion
  • Reference image guided super-resolution method based on dense matching and adaptive fusion

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Embodiment Construction

[0042] The present invention adopts following technical scheme:

[0043] 1) Create a training data set:

[0044]The super-resolution algorithm based on the reference image needs a high-quality reference image as the basis for restoring the missing details, but the image with a high degree of similarity between the reference image and the low-resolution image is difficult to find and has low practical application value, and the similarity is low The actual application value of the pictures is great, but it is difficult to realize full utilization. Therefore, when building a data set, it is necessary to have pictures with a high degree of similarity and pictures with a low degree of similarity. In order to be able to realize practical applications, 84 groups of original pictures in the data set come from Google search and public data building data sets Oxford building, 16 sets of building datasets taken by myself. In addition to one set of data for downsampling as a low-resolut...

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Abstract

The invention belongs to the field of computer vision, relates to an image super-resolution algorithm guided by a reference image, and aims to greatly improve the running speed and the visual result compared with an existing algorithm. The invention discloses a reference image guided super-resolution method based on dense matching and adaptive fusion. The method comprises the following steps: establishing a training data set; aligning the reference image with the low-resolution image; inputting the low-resolution image and the aligned reference image into a convolutional neural network for fusion; setting the learning rate of the network and the weight of the loss function of each part, training the convolutional neural network by using a deep neural network framework PyTorch until the loss converges, and generating a training model; and performing image super-resolution by utilizing the generated training model. The method is mainly applied to computer image processing occasions.

Description

technical field [0001] The invention belongs to the field of computer vision and relates to an image super-resolution algorithm guided by a reference image. Specifically, feature points are extracted through the Scale-invariant feature transform (SIFT) algorithm combined with Random Sample Consensus (RANSAC) to calculate the homography matrix to perform rigid transformation on the reference image, and then through optical flow matching Realize non-rigid transformation, obtain a reference image that is aligned with the low-resolution image as much as possible, and reconstruct the low-resolution image through the encoding and decoding network and similar shape fusion module. Background technique [0002] Super-resolution is a technology that improves the resolution of the original image by means of hardware or software. One or more low-resolution images can be used to obtain a high-resolution image. The current single-image super-resolution has achieved a high PSNR, but there...

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Application Information

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IPC IPC(8): G06T3/40G06N3/04G06N3/08
CPCG06T3/4053G06T3/4007G06N3/08G06N3/045Y02T10/40
Inventor 岳焕景周桐杨敬钰侯春萍
Owner TIANJIN UNIV
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