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Two-channel convolutional neural network-based single image super-resolution calculation method

A convolutional neural network and super-resolution technology, applied in the field of single-image super-resolution computing, can solve problems such as unsatisfactory high-frequency features

Inactive Publication Date: 2017-12-19
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

The SRCNN model proves that it is simple and feasible to directly learn the end-to-end mapping between LR and HR, and the effect is also very good, but the reconstructed high-frequency features are still not satisfactory

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  • Two-channel convolutional neural network-based single image super-resolution calculation method
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Embodiment Construction

[0078] The present invention will be described in detail below in conjunction with the accompanying drawings and implementation examples.

[0079] For the color RGB image, first convert it into a YCbCr image, perform super-resolution reconstruction on the Y component, and use bicubic interpolation to enlarge the Cb and Cr components, and then convert the YCbCr image into an RGB image; for the grayscale image, directly in Super-resolution reconstruction is performed on the grayscale image.

[0080] Such as figure 1 As shown, a single-image super-resolution calculation method based on a dual-channel input convolutional neural network includes the following steps:

[0081] (1) The LR sample image y used for training is interpolated and enlarged to the image X l , image X l Same size as original HR.

[0082] (2) Use the morphological component analysis method to decompose the LR image obtained in step (1), and extract the texture part of the LR image; use the same method to e...

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Abstract

The invention discloses a two-channel convolutional neural network-based single image super-resolution calculation method. The method comprises the steps of (1) performing fuzzy degradation processing on a known high-resolution image to obtain a low-resolution image with the same size; (2) decomposing the obtained low-resolution image after the fuzzy processing in the step (1) into a texture part and a smooth structure part of the low-resolution image, and obtaining a texture part and a smooth structure part of the high-resolution image; (3) combining the low-resolution texture part obtained in the step (2) and an original low-resolution image to obtain a two-channel input, and obtaining an output of the high-resolution texture part; (4) combining the obtained output of the high-resolution texture part in the step (3) and the original low-resolution image to obtain a final image super-resolution reconstruction result, thereby finishing super-resolution reconstruction; and (5) calculating a difference value between the high-resolution texture parts obtained in the steps (4) and (2) to obtain texture part loss, and minimizing a sum of the texture loss and image loss to optimize network structure parameters.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a single-image super-resolution calculation method of a dual-channel convolutional neural network, which is applicable to various computer vision tasks, such as face recognition, target tracking, license plate recognition and the like. Background technique [0002] Single image super-resolution (SR, Super-Resolution) technology refers to the process of restoring a low-resolution (LR, Low-Resolution) image into a high-resolution (HR, High-Resolution) image by software. This technology has a wide range of applications, such as video surveillance, medical imaging, remote sensing satellite imaging, etc. Existing SR rate algorithms can be divided into three categories: interpolation-based [1], reconstruction-based [2] and learning-based methods [3-10]. Among them, the image SR algorithm based on learning learns the functional mapping relationship between LR and H...

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Application Information

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IPC IPC(8): G06T3/40G06T5/00
CPCG06T3/4053G06T2207/20084G06T2207/20081G06T2207/10004G06T5/73
Inventor 李春平贾慧秒周登文
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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