Efficient super-resolution reconstruction method based on deep learning
A super-resolution reconstruction and deep learning technology, applied in neural learning methods, image data processing, instruments, etc., can solve the problems of blurred high-frequency details, difficult super-resolution reconstruction, lack of high-frequency details, etc., to avoid The effect of vanishing gradients, easy training, and high reconstruction quality
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[0039] see Figure 1-6 , in a preferred embodiment of the present invention, an efficient super-resolution reconstruction method based on deep learning, comprising the following steps:
[0040] Step S1: Obtain the commonly used standard data sets for super-resolution. The standard data sets include "91-imags", "Set 5", "Set14", "BSDS100", "manga109", "DIV2K" and "Urban100". For the standard data The concentrated images are rotated, shifted, cut, and scaled to obtain an enhanced dataset.
[0041] Step S2: Read the image information, combine the low-resolution image and the high-resolution image into an information pair, and make a data set required by the network structure.
[0042] Step S3: In the network structure, the input image is subjected to two-branch processing; among them, the first branch uses the nearest neighbor interpolation method to enlarge the low-resolution image to the target size to form an interpolated and up-sampled image, and then proceed to step S8 for ...
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